id	sid	tid	token	lemma	pos
fcis-22610	1	1	frontiers	frontier	NOUN
fcis-22610	1	2	in	in	ADP
fcis-22610	1	3	computing	computing	NOUN
fcis-22610	1	4	and	and	CCONJ
fcis-22610	1	5	intelligent	intelligent	ADJ
fcis-22610	1	6	systems	system	NOUN
fcis-22610	1	7	issn	issn	VERB
fcis-22610	1	8	:	:	PUNCT
fcis-22610	1	9	2832	2832	NUM
fcis-22610	1	10	-	-	SYM
fcis-22610	1	11	6024	6024	NUM
fcis-22610	1	12	|	|	NOUN
fcis-22610	1	13	vol	vol	NOUN
fcis-22610	1	14	.	.	PROPN
fcis-22610	2	1	8	8	NUM
fcis-22610	2	2	,	,	PUNCT
fcis-22610	2	3	no	no	INTJ
fcis-22610	2	4	.	.	NOUN
fcis-22610	2	5	3	3	NUM
fcis-22610	2	6	,	,	PUNCT
fcis-22610	2	7	2024	2024	NUM
fcis-22610	2	8	37	37	NUM
fcis-22610	2	9	contrastive	contrastive	ADJ
fcis-22610	2	10	prediction	prediction	NOUN
fcis-22610	2	11	and	and	CCONJ
fcis-22610	2	12	estimation	estimation	NOUN
fcis-22610	2	13	of	of	ADP
fcis-22610	2	14	deformable	deformable	ADJ
fcis-22610	2	15	objects	object	NOUN
fcis-22610	2	16	based	base	VERB
fcis-22610	2	17	on	on	ADP
fcis-22610	2	18	improved	improve	VERB
fcis-22610	2	19	resnet	resnet	NOUN
fcis-22610	2	20	haipeng	haipeng	NOUN
fcis-22610	2	21	gao	gao	PROPN
fcis-22610	2	22	a	a	PROPN
fcis-22610	2	23	,	,	PUNCT
fcis-22610	2	24	yadong	yadong	ADJ
fcis-22610	2	25	teng	teng	PROPN
fcis-22610	2	26	b	b	PROPN
fcis-22610	2	27	school	school	NOUN
fcis-22610	2	28	of	of	ADP
fcis-22610	2	29	computer	computer	NOUN
fcis-22610	2	30	science	science	NOUN
fcis-22610	2	31	and	and	CCONJ
fcis-22610	2	32	technology	technology	NOUN
fcis-22610	2	33	,	,	PUNCT
fcis-22610	2	34	qingdao	qingdao	PROPN
fcis-22610	2	35	university	university	PROPN
fcis-22610	2	36	,	,	PUNCT
fcis-22610	2	37	qingdao	qingdao	PROPN
fcis-22610	2	38	,	,	PUNCT
fcis-22610	2	39	shandong	shandong	PROPN
fcis-22610	2	40	,	,	PUNCT
fcis-22610	2	41	china	china	PROPN
fcis-22610	2	42	a	a	DET
fcis-22610	2	43	2209309768@qq.com	2209309768@qq.com	NUM
fcis-22610	2	44	,	,	PUNCT
fcis-22610	2	45	b	b	NOUN
fcis-22610	2	46	1912741882@qq.com	1912741882@qq.com	NUM
fcis-22610	2	47	abstract	abstract	NOUN
fcis-22610	2	48	:	:	PUNCT
fcis-22610	2	49	because	because	SCONJ
fcis-22610	2	50	the	the	DET
fcis-22610	2	51	dynamic	dynamic	ADJ
fcis-22610	2	52	model	model	NOUN
fcis-22610	2	53	of	of	ADP
fcis-22610	2	54	deformable	deformable	ADJ
fcis-22610	2	55	linear	linear	ADJ
fcis-22610	2	56	object	object	NOUN
fcis-22610	2	57	is	be	AUX
fcis-22610	2	58	complex	complex	ADJ
fcis-22610	2	59	,	,	PUNCT
fcis-22610	2	60	the	the	DET
fcis-22610	2	61	learning	learning	NOUN
fcis-22610	2	62	based	base	VERB
fcis-22610	2	63	on	on	ADP
fcis-22610	2	64	visual	visual	ADJ
fcis-22610	2	65	model	model	NOUN
fcis-22610	2	66	is	be	AUX
fcis-22610	2	67	difficult	difficult	ADJ
fcis-22610	2	68	,	,	PUNCT
fcis-22610	2	69	and	and	CCONJ
fcis-22610	2	70	the	the	DET
fcis-22610	2	71	feature	feature	NOUN
fcis-22610	2	72	information	information	NOUN
fcis-22610	2	73	extraction	extraction	NOUN
fcis-22610	2	74	is	be	AUX
fcis-22610	2	75	insufficient	insufficient	ADJ
fcis-22610	2	76	.	.	PUNCT
fcis-22610	3	1	therefore	therefore	ADV
fcis-22610	3	2	,	,	PUNCT
fcis-22610	3	3	we	we	PRON
fcis-22610	3	4	propose	propose	VERB
fcis-22610	3	5	a	a	DET
fcis-22610	3	6	joint	joint	ADJ
fcis-22610	3	7	visual	visual	ADJ
fcis-22610	3	8	representation	representation	NOUN
fcis-22610	3	9	model	model	NOUN
fcis-22610	3	10	using	use	VERB
fcis-22610	3	11	contrast	contrast	NOUN
fcis-22610	3	12	learning	learning	NOUN
fcis-22610	3	13	of	of	ADP
fcis-22610	3	14	optimized	optimize	VERB
fcis-22610	3	15	encoder	encoder	NOUN
fcis-22610	3	16	.	.	PUNCT
fcis-22610	4	1	we	we	PRON
fcis-22610	4	2	start	start	VERB
fcis-22610	4	3	with	with	ADP
fcis-22610	4	4	the	the	DET
fcis-22610	4	5	encoder	encoder	NOUN
fcis-22610	4	6	,	,	PUNCT
fcis-22610	4	7	add	add	VERB
fcis-22610	4	8	the	the	DET
fcis-22610	4	9	residual	residual	ADJ
fcis-22610	4	10	structure	structure	NOUN
fcis-22610	4	11	to	to	ADP
fcis-22610	4	12	the	the	DET
fcis-22610	4	13	encoder	encoder	NOUN
fcis-22610	4	14	,	,	PUNCT
fcis-22610	4	15	optimize	optimize	VERB
fcis-22610	4	16	the	the	DET
fcis-22610	4	17	extraction	extraction	NOUN
fcis-22610	4	18	and	and	CCONJ
fcis-22610	4	19	compression	compression	NOUN
fcis-22610	4	20	of	of	ADP
fcis-22610	4	21	its	its	PRON
fcis-22610	4	22	feature	feature	NOUN
fcis-22610	4	23	information	information	NOUN
fcis-22610	4	24	,	,	PUNCT
fcis-22610	4	25	and	and	CCONJ
fcis-22610	4	26	control	control	VERB
fcis-22610	4	27	its	its	PRON
fcis-22610	4	28	parameters	parameter	NOUN
fcis-22610	4	29	to	to	ADP
fcis-22610	4	30	3	3	NUM
fcis-22610	4	31	million	million	NUM
fcis-22610	4	32	.	.	PUNCT
fcis-22610	5	1	in	in	ADP
fcis-22610	5	2	this	this	DET
fcis-22610	5	3	way	way	NOUN
fcis-22610	5	4	,	,	PUNCT
fcis-22610	5	5	we	we	PRON
fcis-22610	5	6	can	can	AUX
fcis-22610	5	7	not	not	PART
fcis-22610	5	8	only	only	ADV
fcis-22610	5	9	obtain	obtain	VERB
fcis-22610	5	10	excellent	excellent	ADJ
fcis-22610	5	11	feature	feature	NOUN
fcis-22610	5	12	information	information	NOUN
fcis-22610	5	13	,	,	PUNCT
fcis-22610	5	14	but	but	CCONJ
fcis-22610	5	15	also	also	ADV
fcis-22610	5	16	have	have	VERB
fcis-22610	5	17	good	good	ADJ
fcis-22610	5	18	efficiency	efficiency	NOUN
fcis-22610	5	19	.	.	PUNCT
fcis-22610	6	1	in	in	ADP
fcis-22610	6	2	the	the	DET
fcis-22610	6	3	rope	rope	NOUN
fcis-22610	6	4	experiment	experiment	NOUN
fcis-22610	6	5	,	,	PUNCT
fcis-22610	6	6	we	we	PRON
fcis-22610	6	7	collect	collect	VERB
fcis-22610	6	8	information	information	NOUN
fcis-22610	6	9	from	from	ADP
fcis-22610	6	10	the	the	DET
fcis-22610	6	11	simulated	simulate	VERB
fcis-22610	6	12	environment	environment	NOUN
fcis-22610	6	13	without	without	ADP
fcis-22610	6	14	manual	manual	ADJ
fcis-22610	6	15	marking	marking	NOUN
fcis-22610	6	16	,	,	PUNCT
fcis-22610	6	17	extract	extract	NOUN
fcis-22610	6	18	features	feature	NOUN
fcis-22610	6	19	through	through	ADP
fcis-22610	6	20	the	the	DET
fcis-22610	6	21	encoder	encoder	NOUN
fcis-22610	6	22	and	and	CCONJ
fcis-22610	6	23	transmit	transmit	VERB
fcis-22610	6	24	them	they	PRON
fcis-22610	6	25	to	to	ADP
fcis-22610	6	26	the	the	DET
fcis-22610	6	27	downstream	downstream	ADJ
fcis-22610	6	28	task	task	NOUN
fcis-22610	6	29	.	.	PUNCT
fcis-22610	7	1	experiments	experiment	NOUN
fcis-22610	7	2	show	show	VERB
fcis-22610	7	3	that	that	SCONJ
fcis-22610	7	4	the	the	DET
fcis-22610	7	5	evaluation	evaluation	NOUN
fcis-22610	7	6	of	of	ADP
fcis-22610	7	7	our	our	PRON
fcis-22610	7	8	model	model	NOUN
fcis-22610	7	9	at	at	ADP
fcis-22610	7	10	135	135	NUM
fcis-22610	7	11	°	°	NUM
fcis-22610	7	12	and	and	CCONJ
fcis-22610	7	13	45	45	NUM
fcis-22610	7	14	°	°	NOUN
fcis-22610	7	15	is	be	AUX
fcis-22610	7	16	improved	improve	VERB
fcis-22610	7	17	by	by	ADP
fcis-22610	7	18	about	about	ADV
fcis-22610	7	19	50	50	NUM
fcis-22610	7	20	%	%	NOUN
fcis-22610	7	21	.	.	PUNCT
fcis-22610	8	1	keywords	keyword	NOUN
fcis-22610	8	2	:	:	PUNCT
fcis-22610	8	3	contrastive	contrastive	ADJ
fcis-22610	8	4	prediction	prediction	NOUN
fcis-22610	8	5	;	;	PUNCT
fcis-22610	8	6	resnet	resnet	NOUN
fcis-22610	8	7	;	;	PUNCT
fcis-22610	8	8	deformable	deformable	ADJ
fcis-22610	8	9	.	.	PUNCT
fcis-22610	9	1	1	1	X
fcis-22610	9	2	.	.	X
fcis-22610	9	3	introduction	introduction	NOUN
fcis-22610	9	4	in	in	ADP
fcis-22610	9	5	the	the	DET
fcis-22610	9	6	past	past	ADJ
fcis-22610	9	7	few	few	ADJ
fcis-22610	9	8	decades	decade	NOUN
fcis-22610	9	9	,	,	PUNCT
fcis-22610	9	10	robot	robot	NOUN
fcis-22610	9	11	arm	arm	NOUN
fcis-22610	9	12	manipulation	manipulation	NOUN
fcis-22610	9	13	has	have	AUX
fcis-22610	9	14	developed	develop	VERB
fcis-22610	9	15	rapidly	rapidly	ADV
fcis-22610	9	16	,	,	PUNCT
fcis-22610	9	17	and	and	CCONJ
fcis-22610	9	18	has	have	AUX
fcis-22610	9	19	made	make	VERB
fcis-22610	9	20	great	great	ADJ
fcis-22610	9	21	progress	progress	NOUN
fcis-22610	9	22	in	in	ADP
fcis-22610	9	23	many	many	ADJ
fcis-22610	9	24	subdivided	subdivide	VERB
fcis-22610	9	25	fields	field	NOUN
fcis-22610	9	26	.	.	PUNCT
fcis-22610	10	1	rigid	rigid	ADJ
fcis-22610	10	2	object	object	NOUN
fcis-22610	10	3	manipulation	manipulation	NOUN
fcis-22610	10	4	is	be	AUX
fcis-22610	10	5	extremely	extremely	ADV
fcis-22610	10	6	mature	mature	ADJ
fcis-22610	10	7	.	.	PUNCT
fcis-22610	11	1	different	different	ADJ
fcis-22610	11	2	from	from	ADP
fcis-22610	11	3	rigid	rigid	ADJ
fcis-22610	11	4	objects	object	NOUN
fcis-22610	11	5	,	,	PUNCT
fcis-22610	11	6	the	the	DET
fcis-22610	11	7	dynamic	dynamic	ADJ
fcis-22610	11	8	model	model	NOUN
fcis-22610	11	9	of	of	ADP
fcis-22610	11	10	deformable	deformable	ADJ
fcis-22610	11	11	objects	object	NOUN
fcis-22610	11	12	is	be	AUX
fcis-22610	11	13	complex	complex	ADJ
fcis-22610	11	14	and	and	CCONJ
fcis-22610	11	15	there	there	PRON
fcis-22610	11	16	is	be	VERB
fcis-22610	11	17	no	no	DET
fcis-22610	11	18	standardized	standardized	ADJ
fcis-22610	11	19	state	state	NOUN
fcis-22610	11	20	,	,	PUNCT
fcis-22610	11	21	so	so	SCONJ
fcis-22610	11	22	the	the	DET
fcis-22610	11	23	operation	operation	NOUN
fcis-22610	11	24	of	of	ADP
fcis-22610	11	25	deformable	deformable	ADJ
fcis-22610	11	26	objects	object	NOUN
fcis-22610	11	27	is	be	AUX
fcis-22610	11	28	still	still	ADV
fcis-22610	11	29	a	a	DET
fcis-22610	11	30	challenging	challenging	ADJ
fcis-22610	11	31	task	task	NOUN
fcis-22610	11	32	,	,	PUNCT
fcis-22610	11	33	and	and	CCONJ
fcis-22610	11	34	the	the	DET
fcis-22610	11	35	control	control	NOUN
fcis-22610	11	36	of	of	ADP
fcis-22610	11	37	deformable	deformable	ADJ
fcis-22610	11	38	linear	linear	ADJ
fcis-22610	11	39	objects	object	NOUN
fcis-22610	11	40	(	(	PUNCT
fcis-22610	11	41	dlo	dlo	NOUN
fcis-22610	11	42	)	)	PUNCT
fcis-22610	11	43	is	be	AUX
fcis-22610	11	44	one	one	NUM
fcis-22610	11	45	of	of	ADP
fcis-22610	11	46	the	the	DET
fcis-22610	11	47	representative	representative	ADJ
fcis-22610	11	48	problems	problem	NOUN
fcis-22610	11	49	.	.	PUNCT
fcis-22610	12	1	the	the	DET
fcis-22610	12	2	manipulation	manipulation	NOUN
fcis-22610	12	3	of	of	ADP
fcis-22610	12	4	deformable	deformable	ADJ
fcis-22610	12	5	objects	object	NOUN
fcis-22610	12	6	has	have	VERB
fcis-22610	12	7	a	a	DET
fcis-22610	12	8	wide	wide	ADJ
fcis-22610	12	9	range	range	NOUN
fcis-22610	12	10	of	of	ADP
fcis-22610	12	11	applications	application	NOUN
fcis-22610	12	12	,	,	PUNCT
fcis-22610	12	13	such	such	ADJ
fcis-22610	12	14	as	as	ADP
fcis-22610	12	15	robotic	robotic	ADJ
fcis-22610	12	16	surgery	surgery	NOUN
fcis-22610	12	17	,	,	PUNCT
fcis-22610	12	18	assisted	assist	VERB
fcis-22610	12	19	dressing	dressing	NOUN
fcis-22610	12	20	,	,	PUNCT
fcis-22610	12	21	cable	cable	NOUN
fcis-22610	12	22	routing	routing	NOUN
fcis-22610	12	23	,	,	PUNCT
fcis-22610	12	24	folding	fold	VERB
fcis-22610	12	25	clothes	clothe	NOUN
fcis-22610	12	26	,	,	PUNCT
fcis-22610	12	27	and	and	CCONJ
fcis-22610	12	28	threading	threading	NOUN
fcis-22610	12	29	in	in	ADP
fcis-22610	12	30	the	the	DET
fcis-22610	12	31	textile	textile	NOUN
fcis-22610	12	32	industry	industry	NOUN
fcis-22610	12	33	[	[	X
fcis-22610	12	34	1	1	NUM
fcis-22610	12	35	,	,	PUNCT
fcis-22610	12	36	2	2	NUM
fcis-22610	12	37	,	,	PUNCT
fcis-22610	12	38	3	3	NUM
fcis-22610	12	39	,	,	PUNCT
fcis-22610	12	40	4	4	NUM
fcis-22610	12	41	,	,	PUNCT
fcis-22610	12	42	5	5	NUM
fcis-22610	12	43	,	,	PUNCT
fcis-22610	12	44	6	6	NUM
fcis-22610	12	45	,	,	PUNCT
fcis-22610	12	46	7	7	NUM
fcis-22610	12	47	,	,	PUNCT
fcis-22610	12	48	8	8	NUM
fcis-22610	12	49	]	]	PUNCT
fcis-22610	12	50	.	.	PUNCT
fcis-22610	13	1	however	however	ADV
fcis-22610	13	2	,	,	PUNCT
fcis-22610	13	3	deformable	deformable	ADJ
fcis-22610	13	4	objects	object	NOUN
fcis-22610	13	5	have	have	VERB
fcis-22610	13	6	complex	complex	ADJ
fcis-22610	13	7	and	and	CCONJ
fcis-22610	13	8	nonlinear	nonlinear	ADJ
fcis-22610	13	9	dynamics	dynamic	NOUN
fcis-22610	13	10	with	with	ADP
fcis-22610	13	11	high	high	ADJ
fcis-22610	13	12	degrees	degree	NOUN
fcis-22610	13	13	of	of	ADP
fcis-22610	13	14	freedom	freedom	NOUN
fcis-22610	13	15	,	,	PUNCT
fcis-22610	13	16	which	which	PRON
fcis-22610	13	17	makes	make	VERB
fcis-22610	13	18	state	state	NOUN
fcis-22610	13	19	estimation	estimation	NOUN
fcis-22610	13	20	challenging	challenge	VERB
fcis-22610	13	21	and	and	CCONJ
fcis-22610	13	22	training	training	NOUN
fcis-22610	13	23	prediction	prediction	NOUN
fcis-22610	13	24	expensive	expensive	ADJ
fcis-22610	13	25	.	.	PUNCT
fcis-22610	14	1	using	use	VERB
fcis-22610	14	2	the	the	DET
fcis-22610	14	3	robotic	robotic	ADJ
fcis-22610	14	4	arm	arm	NOUN
fcis-22610	14	5	,	,	PUNCT
fcis-22610	14	6	placing	place	VERB
fcis-22610	14	7	the	the	DET
fcis-22610	14	8	rope	rope	NOUN
fcis-22610	14	9	from	from	ADP
fcis-22610	14	10	a	a	DET
fcis-22610	14	11	random	random	ADJ
fcis-22610	14	12	initial	initial	ADJ
fcis-22610	14	13	state	state	NOUN
fcis-22610	14	14	to	to	ADP
fcis-22610	14	15	the	the	DET
fcis-22610	14	16	target	target	NOUN
fcis-22610	14	17	state	state	NOUN
fcis-22610	14	18	,	,	PUNCT
fcis-22610	14	19	or	or	CCONJ
fcis-22610	14	20	using	use	VERB
fcis-22610	14	21	a	a	DET
fcis-22610	14	22	two	two	NUM
fcis-22610	14	23	-	-	PUNCT
fcis-22610	14	24	arm	arm	NOUN
fcis-22610	14	25	robot	robot	NOUN
fcis-22610	14	26	to	to	PART
fcis-22610	14	27	tie	tie	VERB
fcis-22610	14	28	the	the	DET
fcis-22610	14	29	rope	rope	NOUN
fcis-22610	14	30	,	,	PUNCT
fcis-22610	14	31	all	all	PRON
fcis-22610	14	32	of	of	ADP
fcis-22610	14	33	which	which	PRON
fcis-22610	14	34	require	require	VERB
fcis-22610	14	35	the	the	DET
fcis-22610	14	36	robot	robot	NOUN
fcis-22610	14	37	to	to	PART
fcis-22610	14	38	observe	observe	VERB
fcis-22610	14	39	and	and	CCONJ
fcis-22610	14	40	generate	generate	VERB
fcis-22610	14	41	corresponding	corresponding	ADJ
fcis-22610	14	42	actions	action	NOUN
fcis-22610	14	43	to	to	PART
fcis-22610	14	44	operate	operate	VERB
fcis-22610	14	45	according	accord	VERB
fcis-22610	14	46	to	to	ADP
fcis-22610	14	47	the	the	DET
fcis-22610	14	48	current	current	ADJ
fcis-22610	14	49	state	state	NOUN
fcis-22610	14	50	of	of	ADP
fcis-22610	14	51	the	the	DET
fcis-22610	14	52	rope	rope	NOUN
fcis-22610	14	53	.	.	PUNCT
fcis-22610	15	1	the	the	DET
fcis-22610	15	2	dynamic	dynamic	ADJ
fcis-22610	15	3	model	model	NOUN
fcis-22610	15	4	of	of	ADP
fcis-22610	15	5	deformable	deformable	ADJ
fcis-22610	15	6	objects	object	NOUN
fcis-22610	15	7	is	be	AUX
fcis-22610	15	8	complex	complex	ADJ
fcis-22610	15	9	and	and	CCONJ
fcis-22610	15	10	nonlinear	nonlinear	ADJ
fcis-22610	15	11	,	,	PUNCT
fcis-22610	15	12	and	and	CCONJ
fcis-22610	15	13	even	even	ADV
fcis-22610	15	14	simple	simple	ADJ
fcis-22610	15	15	objects	object	NOUN
fcis-22610	15	16	face	face	VERB
fcis-22610	15	17	complex	complex	ADJ
fcis-22610	15	18	and	and	CCONJ
fcis-22610	15	19	unpredictable	unpredictable	ADJ
fcis-22610	15	20	behavior	behavior	NOUN
fcis-22610	15	21	.	.	PUNCT
fcis-22610	16	1	we	we	PRON
fcis-22610	16	2	choose	choose	VERB
fcis-22610	16	3	the	the	DET
fcis-22610	16	4	learning	learning	NOUN
fcis-22610	16	5	based	base	VERB
fcis-22610	16	6	method	method	NOUN
fcis-22610	16	7	among	among	ADP
fcis-22610	16	8	many	many	ADJ
fcis-22610	16	9	methods	method	NOUN
fcis-22610	16	10	,	,	PUNCT
fcis-22610	16	11	but	but	CCONJ
fcis-22610	16	12	if	if	SCONJ
fcis-22610	16	13	there	there	PRON
fcis-22610	16	14	is	be	VERB
fcis-22610	16	15	no	no	DET
fcis-22610	16	16	excellent	excellent	ADJ
fcis-22610	16	17	model	model	NOUN
fcis-22610	16	18	,	,	PUNCT
fcis-22610	16	19	its	its	PRON
fcis-22610	16	20	effect	effect	NOUN
fcis-22610	16	21	is	be	AUX
fcis-22610	16	22	general	general	ADJ
fcis-22610	16	23	and	and	CCONJ
fcis-22610	16	24	generalization	generalization	NOUN
fcis-22610	16	25	(	(	PUNCT
fcis-22610	16	26	the	the	DET
fcis-22610	16	27	purpose	purpose	NOUN
fcis-22610	16	28	of	of	ADP
fcis-22610	16	29	learning	learning	NOUN
fcis-22610	16	30	is	be	AUX
fcis-22610	16	31	to	to	PART
fcis-22610	16	32	learn	learn	VERB
fcis-22610	16	33	the	the	DET
fcis-22610	16	34	laws	law	NOUN
fcis-22610	16	35	hidden	hide	VERB
fcis-22610	16	36	behind	behind	ADP
fcis-22610	16	37	the	the	DET
fcis-22610	16	38	data	datum	NOUN
fcis-22610	16	39	,	,	PUNCT
fcis-22610	16	40	and	and	CCONJ
fcis-22610	16	41	the	the	DET
fcis-22610	16	42	trained	train	VERB
fcis-22610	16	43	network	network	NOUN
fcis-22610	16	44	can	can	AUX
fcis-22610	16	45	also	also	ADV
fcis-22610	16	46	give	give	VERB
fcis-22610	16	47	appropriate	appropriate	ADJ
fcis-22610	16	48	output	output	NOUN
fcis-22610	16	49	for	for	ADP
fcis-22610	16	50	the	the	DET
fcis-22610	16	51	data	datum	NOUN
fcis-22610	16	52	other	other	ADJ
fcis-22610	16	53	than	than	ADP
fcis-22610	16	54	the	the	DET
fcis-22610	16	55	learning	learning	NOUN
fcis-22610	16	56	set	set	VERB
fcis-22610	16	57	with	with	ADP
fcis-22610	16	58	the	the	DET
fcis-22610	16	59	same	same	ADJ
fcis-22610	16	60	law	law	NOUN
fcis-22610	16	61	)	)	PUNCT
fcis-22610	16	62	is	be	AUX
fcis-22610	16	63	weak.[9	weak.[9	NOUN
fcis-22610	16	64	,	,	PUNCT
fcis-22610	16	65	10	10	NUM
fcis-22610	16	66	,	,	PUNCT
fcis-22610	16	67	11	11	NUM
fcis-22610	16	68	]	]	PUNCT
fcis-22610	16	69	with	with	ADP
fcis-22610	16	70	the	the	DET
fcis-22610	16	71	introduction	introduction	NOUN
fcis-22610	16	72	and	and	CCONJ
fcis-22610	16	73	improvement	improvement	NOUN
fcis-22610	16	74	of	of	ADP
fcis-22610	16	75	elf	elf	ADV
fcis-22610	16	76	-	-	PUNCT
fcis-22610	16	77	supervised	supervised	ADJ
fcis-22610	16	78	learning	learning	NOUN
fcis-22610	16	79	,	,	PUNCT
fcis-22610	16	80	elf	elf	PRON
fcis-22610	16	81	-	-	PUNCT
fcis-22610	16	82	supervised	supervised	ADJ
fcis-22610	16	83	learning	learning	NOUN
fcis-22610	16	84	directly	directly	ADV
fcis-22610	16	85	uses	use	VERB
fcis-22610	16	86	the	the	DET
fcis-22610	16	87	data	datum	NOUN
fcis-22610	16	88	itself	itself	PRON
fcis-22610	16	89	to	to	PART
fcis-22610	16	90	provide	provide	VERB
fcis-22610	16	91	supervision	supervision	NOUN
fcis-22610	16	92	information	information	NOUN
fcis-22610	16	93	to	to	PART
fcis-22610	16	94	guide	guide	VERB
fcis-22610	16	95	learning	learning	NOUN
fcis-22610	16	96	,	,	PUNCT
fcis-22610	16	97	so	so	ADV
fcis-22610	16	98	its	its	PRON
fcis-22610	16	99	training	training	NOUN
fcis-22610	16	100	cost	cost	NOUN
fcis-22610	16	101	is	be	AUX
fcis-22610	16	102	reduced	reduce	VERB
fcis-22610	16	103	.	.	PUNCT
fcis-22610	17	1	the	the	DET
fcis-22610	17	2	emergence	emergence	NOUN
fcis-22610	17	3	of	of	ADP
fcis-22610	17	4	contrast	contrast	NOUN
fcis-22610	17	5	learning	learn	VERB
fcis-22610	17	6	method	method	NOUN
fcis-22610	17	7	based	base	VERB
fcis-22610	17	8	on	on	ADP
fcis-22610	17	9	self	self	NOUN
fcis-22610	17	10	supervised	supervised	ADJ
fcis-22610	17	11	learning	learn	VERB
fcis-22610	17	12	not	not	PART
fcis-22610	17	13	only	only	ADV
fcis-22610	17	14	reduces	reduce	VERB
fcis-22610	17	15	the	the	DET
fcis-22610	17	16	training	training	NOUN
fcis-22610	17	17	cost	cost	NOUN
fcis-22610	17	18	of	of	ADP
fcis-22610	17	19	images	image	NOUN
fcis-22610	17	20	and	and	CCONJ
fcis-22610	17	21	labels	label	NOUN
fcis-22610	17	22	,	,	PUNCT
fcis-22610	17	23	but	but	CCONJ
fcis-22610	17	24	also	also	ADV
fcis-22610	17	25	can	can	AUX
fcis-22610	17	26	extract	extract	VERB
fcis-22610	17	27	a	a	DET
fcis-22610	17	28	general	general	ADJ
fcis-22610	17	29	and	and	CCONJ
fcis-22610	17	30	easy	easy	ADJ
fcis-22610	17	31	to	to	PART
fcis-22610	17	32	transform	transform	VERB
fcis-22610	17	33	feature	feature	NOUN
fcis-22610	17	34	model	model	NOUN
fcis-22610	17	35	.	.	PUNCT
fcis-22610	18	1	in	in	ADP
fcis-22610	18	2	the	the	DET
fcis-22610	18	3	shape	shape	NOUN
fcis-22610	18	4	control	control	NOUN
fcis-22610	18	5	of	of	ADP
fcis-22610	18	6	dlo	dlo	PROPN
fcis-22610	18	7	,	,	PUNCT
fcis-22610	18	8	the	the	DET
fcis-22610	18	9	deformable	deformable	ADJ
fcis-22610	18	10	object	object	NOUN
fcis-22610	18	11	prediction	prediction	NOUN
fcis-22610	18	12	representation	representation	NOUN
fcis-22610	18	13	learning	learning	NOUN
fcis-22610	18	14	proposed	propose	VERB
fcis-22610	18	15	by	by	ADP
fcis-22610	18	16	wilson	wilson	PROPN
fcis-22610	18	17	yan	yan	PROPN
fcis-22610	19	1	[	[	X
fcis-22610	19	2	12	12	NUM
fcis-22610	19	3	]	]	PUNCT
fcis-22610	19	4	et	et	PROPN
fcis-22610	19	5	al	al	PROPN
fcis-22610	19	6	.	.	PROPN
fcis-22610	20	1	based	base	VERB
fcis-22610	20	2	on	on	ADP
fcis-22610	20	3	contrast	contrast	NOUN
fcis-22610	20	4	estimation	estimation	NOUN
fcis-22610	20	5	is	be	AUX
fcis-22610	20	6	feasible	feasible	ADJ
fcis-22610	20	7	.	.	PUNCT
fcis-22610	21	1	however	however	ADV
fcis-22610	21	2	,	,	PUNCT
fcis-22610	21	3	due	due	ADP
fcis-22610	21	4	to	to	ADP
fcis-22610	21	5	its	its	PRON
fcis-22610	21	6	predictive	predictive	ADJ
fcis-22610	21	7	control	control	NOUN
fcis-22610	21	8	from	from	ADP
fcis-22610	21	9	various	various	ADJ
fcis-22610	21	10	angles	angle	NOUN
fcis-22610	21	11	,	,	PUNCT
fcis-22610	21	12	its	its	PRON
fcis-22610	21	13	final	final	ADJ
fcis-22610	21	14	state	state	NOUN
fcis-22610	21	15	is	be	AUX
fcis-22610	21	16	easy	easy	ADJ
fcis-22610	21	17	to	to	PART
fcis-22610	21	18	have	have	VERB
fcis-22610	21	19	a	a	DET
fcis-22610	21	20	large	large	ADJ
fcis-22610	21	21	difference	difference	NOUN
fcis-22610	21	22	from	from	ADP
fcis-22610	21	23	the	the	DET
fcis-22610	21	24	target	target	NOUN
fcis-22610	21	25	state	state	NOUN
fcis-22610	21	26	.	.	PUNCT
fcis-22610	22	1	we	we	PRON
fcis-22610	22	2	speculate	speculate	VERB
fcis-22610	22	3	that	that	SCONJ
fcis-22610	22	4	the	the	DET
fcis-22610	22	5	extraction	extraction	NOUN
fcis-22610	22	6	of	of	ADP
fcis-22610	22	7	feature	feature	NOUN
fcis-22610	22	8	information	information	NOUN
fcis-22610	22	9	is	be	AUX
fcis-22610	22	10	not	not	PART
fcis-22610	22	11	perfect	perfect	ADJ
fcis-22610	22	12	and	and	CCONJ
fcis-22610	22	13	the	the	DET
fcis-22610	22	14	depth	depth	NOUN
fcis-22610	22	15	of	of	ADP
fcis-22610	22	16	the	the	DET
fcis-22610	22	17	model	model	NOUN
fcis-22610	22	18	is	be	AUX
fcis-22610	22	19	not	not	PART
fcis-22610	22	20	enough	enough	ADJ
fcis-22610	22	21	.	.	PUNCT
fcis-22610	23	1	t	t	NOUN
fcis-22610	23	2	therefore	therefore	ADV
fcis-22610	23	3	,	,	PUNCT
fcis-22610	23	4	on	on	ADP
fcis-22610	23	5	this	this	DET
fcis-22610	23	6	basis	basis	NOUN
fcis-22610	23	7	,	,	PUNCT
fcis-22610	23	8	a	a	DET
fcis-22610	23	9	new	new	ADJ
fcis-22610	23	10	network	network	NOUN
fcis-22610	23	11	structure	structure	NOUN
fcis-22610	23	12	based	base	VERB
fcis-22610	23	13	on	on	ADP
fcis-22610	23	14	comparative	comparative	ADJ
fcis-22610	23	15	learning	learning	NOUN
fcis-22610	23	16	is	be	AUX
fcis-22610	23	17	proposed	propose	VERB
fcis-22610	23	18	to	to	PART
fcis-22610	23	19	deal	deal	VERB
fcis-22610	23	20	with	with	ADP
fcis-22610	23	21	deformable	deformable	ADJ
fcis-22610	23	22	linear	linear	ADJ
fcis-22610	23	23	objects	object	NOUN
fcis-22610	23	24	,	,	PUNCT
fcis-22610	23	25	improve	improve	VERB
fcis-22610	23	26	the	the	DET
fcis-22610	23	27	ability	ability	NOUN
fcis-22610	23	28	of	of	ADP
fcis-22610	23	29	feature	feature	NOUN
fcis-22610	23	30	information	information	NOUN
fcis-22610	23	31	extraction	extraction	NOUN
fcis-22610	23	32	and	and	CCONJ
fcis-22610	23	33	optimize	optimize	VERB
fcis-22610	23	34	the	the	DET
fcis-22610	23	35	depth	depth	NOUN
fcis-22610	23	36	of	of	ADP
fcis-22610	23	37	the	the	DET
fcis-22610	23	38	model	model	NOUN
fcis-22610	23	39	.	.	PUNCT
fcis-22610	24	1	in	in	ADP
fcis-22610	24	2	terms	term	NOUN
fcis-22610	24	3	of	of	ADP
fcis-22610	24	4	dlo	dlo	PROPN
fcis-22610	24	5	operation	operation	NOUN
fcis-22610	24	6	,	,	PUNCT
fcis-22610	24	7	the	the	DET
fcis-22610	24	8	requirements	requirement	NOUN
fcis-22610	24	9	for	for	ADP
fcis-22610	24	10	accuracy	accuracy	NOUN
fcis-22610	24	11	are	be	AUX
fcis-22610	24	12	also	also	ADV
fcis-22610	24	13	increasing	increase	VERB
fcis-22610	24	14	.	.	PUNCT
fcis-22610	25	1	in	in	ADP
fcis-22610	25	2	this	this	DET
fcis-22610	25	3	paper	paper	NOUN
fcis-22610	25	4	,	,	PUNCT
fcis-22610	25	5	with	with	ADP
fcis-22610	25	6	the	the	DET
fcis-22610	25	7	popularity	popularity	NOUN
fcis-22610	25	8	of	of	ADP
fcis-22610	25	9	many	many	ADJ
fcis-22610	25	10	efficient	efficient	ADJ
fcis-22610	25	11	frameworks	framework	NOUN
fcis-22610	25	12	and	and	CCONJ
fcis-22610	25	13	the	the	DET
fcis-22610	25	14	open	open	ADJ
fcis-22610	25	15	source	source	NOUN
fcis-22610	25	16	of	of	ADP
fcis-22610	25	17	various	various	ADJ
fcis-22610	25	18	network	network	NOUN
fcis-22610	25	19	structures	structure	NOUN
fcis-22610	25	20	,	,	PUNCT
fcis-22610	25	21	we	we	PRON
fcis-22610	25	22	learn	learn	VERB
fcis-22610	25	23	a	a	DET
fcis-22610	25	24	variety	variety	NOUN
fcis-22610	25	25	of	of	ADP
fcis-22610	25	26	frameworks	framework	NOUN
fcis-22610	25	27	and	and	CCONJ
fcis-22610	25	28	train	train	VERB
fcis-22610	25	29	a	a	DET
fcis-22610	25	30	new	new	ADJ
fcis-22610	25	31	framework	framework	NOUN
fcis-22610	25	32	based	base	VERB
fcis-22610	25	33	on	on	ADP
fcis-22610	25	34	visual	visual	ADJ
fcis-22610	25	35	model	model	NOUN
fcis-22610	25	36	.	.	PUNCT
fcis-22610	26	1	the	the	DET
fcis-22610	26	2	framework	framework	NOUN
fcis-22610	26	3	jointly	jointly	ADV
fcis-22610	26	4	learns	learn	VERB
fcis-22610	26	5	visual	visual	ADJ
fcis-22610	26	6	representations	representation	NOUN
fcis-22610	26	7	of	of	ADP
fcis-22610	26	8	latent	latent	NOUN
fcis-22610	26	9	spaces	space	NOUN
fcis-22610	26	10	and	and	CCONJ
fcis-22610	26	11	dynamic	dynamic	ADJ
fcis-22610	26	12	models	model	NOUN
fcis-22610	26	13	of	of	ADP
fcis-22610	26	14	deformable	deformable	ADJ
fcis-22610	26	15	objects	object	NOUN
fcis-22610	26	16	using	use	VERB
fcis-22610	26	17	contrastive	contrastive	ADJ
fcis-22610	26	18	optimization	optimization	NOUN
fcis-22610	26	19	.	.	PUNCT
fcis-22610	27	1	inspired	inspire	VERB
fcis-22610	27	2	by	by	ADP
fcis-22610	27	3	simclr	simclr	NOUN
fcis-22610	27	4	[	[	X
fcis-22610	27	5	13	13	NUM
fcis-22610	27	6	]	]	PUNCT
fcis-22610	27	7	and	and	CCONJ
fcis-22610	27	8	resnet	resnet	VERB
fcis-22610	27	9	[	[	X
fcis-22610	27	10	14	14	NUM
fcis-22610	27	11	]	]	X
fcis-22610	27	12	,	,	PUNCT
fcis-22610	27	13	a	a	DET
fcis-22610	27	14	special	special	ADJ
fcis-22610	27	15	deep	deep	ADJ
fcis-22610	27	16	convolutional	convolutional	ADJ
fcis-22610	27	17	network	network	NOUN
fcis-22610	27	18	with	with	ADP
fcis-22610	27	19	a	a	DET
fcis-22610	27	20	separable	separable	ADJ
fcis-22610	27	21	residual	residual	ADJ
fcis-22610	27	22	network	network	NOUN
fcis-22610	27	23	is	be	AUX
fcis-22610	27	24	added	add	VERB
fcis-22610	27	25	to	to	ADP
fcis-22610	27	26	the	the	DET
fcis-22610	27	27	extraction	extraction	NOUN
fcis-22610	27	28	of	of	ADP
fcis-22610	27	29	features	feature	NOUN
fcis-22610	27	30	from	from	ADP
fcis-22610	27	31	the	the	DET
fcis-22610	27	32	encoder	encoder	NOUN
fcis-22610	27	33	to	to	ADP
fcis-22610	27	34	the	the	DET
fcis-22610	27	35	latent	latent	NOUN
fcis-22610	27	36	space	space	NOUN
fcis-22610	27	37	,	,	PUNCT
fcis-22610	27	38	which	which	PRON
fcis-22610	27	39	not	not	PART
fcis-22610	27	40	only	only	ADV
fcis-22610	27	41	improves	improve	VERB
fcis-22610	27	42	the	the	DET
fcis-22610	27	43	efficiency	efficiency	NOUN
fcis-22610	27	44	of	of	ADP
fcis-22610	27	45	feature	feature	NOUN
fcis-22610	27	46	extraction	extraction	NOUN
fcis-22610	27	47	and	and	CCONJ
fcis-22610	27	48	quality	quality	NOUN
fcis-22610	27	49	,	,	PUNCT
fcis-22610	27	50	and	and	CCONJ
fcis-22610	27	51	balances	balance	VERB
fcis-22610	27	52	the	the	DET
fcis-22610	27	53	speed	speed	NOUN
fcis-22610	27	54	and	and	CCONJ
fcis-22610	27	55	quality	quality	NOUN
fcis-22610	27	56	of	of	ADP
fcis-22610	27	57	feature	feature	NOUN
fcis-22610	27	58	extraction	extraction	NOUN
fcis-22610	27	59	.	.	PUNCT
fcis-22610	28	1	the	the	DET
fcis-22610	28	2	residual	residual	ADJ
fcis-22610	28	3	network	network	NOUN
fcis-22610	28	4	structure	structure	NOUN
fcis-22610	28	5	can	can	AUX
fcis-22610	28	6	be	be	AUX
fcis-22610	28	7	added	add	VERB
fcis-22610	28	8	to	to	ADP
fcis-22610	28	9	most	most	ADJ
fcis-22610	28	10	neural	neural	ADJ
fcis-22610	28	11	networks	network	NOUN
fcis-22610	28	12	to	to	PART
fcis-22610	28	13	effectively	effectively	ADV
fcis-22610	28	14	solve	solve	VERB
fcis-22610	28	15	the	the	DET
fcis-22610	28	16	problems	problem	NOUN
fcis-22610	28	17	such	such	ADJ
fcis-22610	28	18	as	as	ADP
fcis-22610	28	19	gradient	gradient	ADJ
fcis-22610	28	20	explosion	explosion	NOUN
fcis-22610	28	21	,	,	PUNCT
fcis-22610	28	22	learn	learn	VERB
fcis-22610	28	23	effective	effective	ADJ
fcis-22610	28	24	model	model	NOUN
fcis-22610	28	25	dynamics	dynamic	NOUN
fcis-22610	28	26	after	after	ADP
fcis-22610	28	27	feature	feature	NOUN
fcis-22610	28	28	extraction	extraction	NOUN
fcis-22610	28	29	,	,	PUNCT
fcis-22610	28	30	and	and	CCONJ
fcis-22610	28	31	use	use	VERB
fcis-22610	28	32	standard	standard	ADJ
fcis-22610	28	33	model	model	NOUN
fcis-22610	28	34	predictive	predictive	PROPN
fcis-22610	28	35	control	control	PROPN
fcis-22610	28	36	(	(	PUNCT
fcis-22610	28	37	mpc	mpc	NOUN
fcis-22610	28	38	)	)	PUNCT
fcis-22610	28	39	and	and	CCONJ
fcis-22610	28	40	one	one	NUM
fcis-22610	28	41	-	-	PUNCT
fcis-22610	28	42	step	step	NOUN
fcis-22610	28	43	prediction	prediction	NOUN
fcis-22610	28	44	to	to	PART
fcis-22610	28	45	operate	operate	VERB
fcis-22610	28	46	deformable	deformable	ADJ
fcis-22610	28	47	objects	object	NOUN
fcis-22610	28	48	and	and	CCONJ
fcis-22610	28	49	let	let	VERB
fcis-22610	28	50	them	they	PRON
fcis-22610	28	51	reach	reach	VERB
fcis-22610	28	52	the	the	DET
fcis-22610	28	53	target	target	NOUN
fcis-22610	28	54	position	position	NOUN
fcis-22610	28	55	.	.	PUNCT
fcis-22610	29	1	in	in	ADP
fcis-22610	29	2	experiments	experiment	NOUN
fcis-22610	29	3	,	,	PUNCT
fcis-22610	29	4	we	we	PRON
fcis-22610	29	5	have	have	AUX
fcis-22610	29	6	made	make	VERB
fcis-22610	29	7	considerable	considerable	ADJ
fcis-22610	29	8	progress	progress	NOUN
fcis-22610	29	9	compared	compare	VERB
fcis-22610	29	10	with	with	ADP
fcis-22610	29	11	other	other	ADJ
fcis-22610	29	12	baseline	baseline	NOUN
fcis-22610	29	13	methods	method	NOUN
fcis-22610	29	14	.	.	PUNCT
fcis-22610	30	1	in	in	ADP
fcis-22610	30	2	summary	summary	NOUN
fcis-22610	30	3	,	,	PUNCT
fcis-22610	30	4	the	the	DET
fcis-22610	30	5	main	main	ADJ
fcis-22610	30	6	contributions	contribution	NOUN
fcis-22610	30	7	of	of	ADP
fcis-22610	30	8	this	this	DET
fcis-22610	30	9	paper	paper	NOUN
fcis-22610	30	10	can	can	AUX
fcis-22610	30	11	be	be	AUX
fcis-22610	30	12	summarized	summarize	VERB
fcis-22610	30	13	as	as	SCONJ
fcis-22610	30	14	follows	follow	VERB
fcis-22610	30	15	:	:	PUNCT
fcis-22610	30	16	(	(	PUNCT
fcis-22610	30	17	a	a	X
fcis-22610	30	18	)	)	PUNCT
fcis-22610	30	19	we	we	PRON
fcis-22610	30	20	propose	propose	VERB
fcis-22610	30	21	a	a	DET
fcis-22610	30	22	dlo	dlo	NOUN
fcis-22610	30	23	shape	shape	NOUN
fcis-22610	30	24	control	control	NOUN
fcis-22610	30	25	compatible	compatible	ADJ
fcis-22610	30	26	model	model	NOUN
fcis-22610	30	27	predictive	predictive	PROPN
fcis-22610	30	28	control	control	PROPN
fcis-22610	30	29	method	method	NOUN
fcis-22610	30	30	based	base	VERB
fcis-22610	30	31	on	on	ADP
fcis-22610	30	32	comparative	comparative	ADJ
fcis-22610	30	33	learning	learning	NOUN
fcis-22610	30	34	(	(	PUNCT
fcis-22610	30	35	b	b	NOUN
fcis-22610	30	36	)	)	PUNCT
fcis-22610	30	37	after	after	ADP
fcis-22610	30	38	modifying	modify	VERB
fcis-22610	30	39	the	the	DET
fcis-22610	30	40	network	network	NOUN
fcis-22610	30	41	structure	structure	NOUN
fcis-22610	30	42	,	,	PUNCT
fcis-22610	30	43	compared	compare	VERB
fcis-22610	30	44	with	with	ADP
fcis-22610	30	45	other	other	ADJ
fcis-22610	30	46	baseline	baseline	NOUN
fcis-22610	30	47	methods	method	NOUN
fcis-22610	30	48	,	,	PUNCT
fcis-22610	30	49	we	we	PRON
fcis-22610	30	50	find	find	VERB
fcis-22610	30	51	that	that	SCONJ
fcis-22610	30	52	our	our	PRON
fcis-22610	30	53	method	method	NOUN
fcis-22610	30	54	has	have	VERB
fcis-22610	30	55	the	the	DET
fcis-22610	30	56	potential	potential	ADJ
fcis-22610	30	57	representation	representation	NOUN
fcis-22610	30	58	of	of	ADP
fcis-22610	30	59	stronger	strong	ADJ
fcis-22610	30	60	learning	learning	NOUN
fcis-22610	30	61	and	and	CCONJ
fcis-22610	30	62	more	more	ADJ
fcis-22610	30	63	planning	planning	NOUN
fcis-22610	30	64	,	,	PUNCT
fcis-22610	30	65	and	and	CCONJ
fcis-22610	30	66	its	its	PRON
fcis-22610	30	67	feature	feature	NOUN
fcis-22610	30	68	information	information	NOUN
fcis-22610	30	69	extraction	extraction	NOUN
fcis-22610	30	70	has	have	VERB
fcis-22610	30	71	more	more	ADJ
fcis-22610	30	72	advantages	advantage	NOUN
fcis-22610	30	73	in	in	ADP
fcis-22610	30	74	this	this	DET
fcis-22610	30	75	paper	paper	NOUN
fcis-22610	30	76	,	,	PUNCT
fcis-22610	30	77	we	we	PRON
fcis-22610	30	78	detail	detail	VERB
fcis-22610	30	79	the	the	DET
fcis-22610	30	80	shape	shape	NOUN
fcis-22610	30	81	control	control	NOUN
fcis-22610	30	82	of	of	ADP
fcis-22610	30	83	deformable	deformable	ADJ
fcis-22610	30	84	objects	object	NOUN
fcis-22610	30	85	based	base	VERB
fcis-22610	30	86	on	on	ADP
fcis-22610	30	87	a	a	DET
fcis-22610	30	88	contrastive	contrastive	ADJ
fcis-22610	30	89	learning	learning	NOUN
fcis-22610	30	90	method	method	NOUN
fcis-22610	30	91	with	with	ADP
fcis-22610	30	92	an	an	DET
fcis-22610	30	93	improved	improved	ADJ
fcis-22610	30	94	network	network	NOUN
fcis-22610	30	95	structure	structure	NOUN
fcis-22610	30	96	,	,	PUNCT
fcis-22610	30	97	and	and	CCONJ
fcis-22610	30	98	we	we	PRON
fcis-22610	30	99	organize	organize	VERB
fcis-22610	30	100	the	the	DET
fcis-22610	30	101	rest	rest	NOUN
fcis-22610	30	102	of	of	ADP
fcis-22610	30	103	this	this	DET
fcis-22610	30	104	paper	paper	NOUN
fcis-22610	30	105	as	as	SCONJ
fcis-22610	30	106	follows	follow	VERB
fcis-22610	30	107	.	.	PUNCT
fcis-22610	31	1	in	in	ADP
fcis-22610	31	2	section	section	PROPN
fcis-22610	31	3	ii	ii	PROPN
fcis-22610	31	4	,	,	PUNCT
fcis-22610	31	5	were	be	AUX
fcis-22610	31	6	view	view	VERB
fcis-22610	31	7	the	the	DET
fcis-22610	31	8	work	work	NOUN
fcis-22610	31	9	related	relate	VERB
fcis-22610	31	10	to	to	ADP
fcis-22610	31	11	our	our	PRON
fcis-22610	31	12	method	method	NOUN
fcis-22610	31	13	.	.	PUNCT
fcis-22610	32	1	in	in	ADP
fcis-22610	32	2	the	the	DET
fcis-22610	32	3	third	third	ADJ
fcis-22610	32	4	part	part	NOUN
fcis-22610	32	5	,	,	PUNCT
fcis-22610	32	6	we	we	PRON
fcis-22610	32	7	present	present	VERB
fcis-22610	32	8	our	our	PRON
fcis-22610	32	9	model	model	NOUN
fcis-22610	32	10	in	in	ADP
fcis-22610	32	11	detail	detail	NOUN
fcis-22610	32	12	,	,	PUNCT
fcis-22610	32	13	including	include	VERB
fcis-22610	32	14	a	a	DET
fcis-22610	32	15	detailed	detailed	ADJ
fcis-22610	32	16	description	description	NOUN
fcis-22610	32	17	of	of	ADP
fcis-22610	32	18	some	some	PRON
fcis-22610	32	19	of	of	ADP
fcis-22610	32	20	the	the	DET
fcis-22610	32	21	components	component	NOUN
fcis-22610	32	22	38	38	NUM
fcis-22610	32	23	used	use	VERB
fcis-22610	32	24	in	in	ADP
fcis-22610	32	25	the	the	DET
fcis-22610	32	26	method	method	NOUN
fcis-22610	32	27	,	,	PUNCT
fcis-22610	32	28	as	as	ADV
fcis-22610	32	29	well	well	ADV
fcis-22610	32	30	as	as	ADP
fcis-22610	32	31	the	the	DET
fcis-22610	32	32	mathematical	mathematical	ADJ
fcis-22610	32	33	formulations	formulation	NOUN
fcis-22610	32	34	and	and	CCONJ
fcis-22610	32	35	parameters	parameter	NOUN
fcis-22610	32	36	used	use	VERB
fcis-22610	32	37	in	in	ADP
fcis-22610	32	38	the	the	DET
fcis-22610	32	39	code	code	NOUN
fcis-22610	32	40	implementation	implementation	NOUN
fcis-22610	32	41	.	.	PUNCT
fcis-22610	33	1	in	in	ADP
fcis-22610	33	2	section	section	NOUN
fcis-22610	33	3	iv	iv	NUM
fcis-22610	33	4	,	,	PUNCT
fcis-22610	33	5	we	we	PRON
fcis-22610	33	6	have	have	AUX
fcis-22610	33	7	carried	carry	VERB
fcis-22610	33	8	out	out	ADP
fcis-22610	33	9	experiments	experiment	NOUN
fcis-22610	33	10	on	on	ADP
fcis-22610	33	11	a	a	DET
fcis-22610	33	12	data	data	NOUN
fcis-22610	33	13	set	set	VERB
fcis-22610	33	14	made	make	VERB
fcis-22610	33	15	of	of	ADP
fcis-22610	33	16	collected	collect	VERB
fcis-22610	33	17	data	datum	NOUN
fcis-22610	33	18	in	in	ADP
fcis-22610	33	19	the	the	DET
fcis-22610	33	20	simulation	simulation	NOUN
fcis-22610	33	21	environment	environment	NOUN
fcis-22610	33	22	,	,	PUNCT
fcis-22610	33	23	and	and	CCONJ
fcis-22610	33	24	compared	compare	VERB
fcis-22610	33	25	it	it	PRON
fcis-22610	33	26	with	with	ADP
fcis-22610	33	27	some	some	DET
fcis-22610	33	28	baseline	baseline	ADJ
fcis-22610	33	29	methods	method	NOUN
fcis-22610	33	30	to	to	PART
fcis-22610	33	31	show	show	VERB
fcis-22610	33	32	that	that	SCONJ
fcis-22610	33	33	our	our	PRON
fcis-22610	33	34	method	method	NOUN
fcis-22610	33	35	is	be	AUX
fcis-22610	33	36	relatively	relatively	ADV
fcis-22610	33	37	advanced	advanced	ADJ
fcis-22610	33	38	.	.	PUNCT
fcis-22610	34	1	at	at	ADP
fcis-22610	34	2	the	the	DET
fcis-22610	34	3	end	end	NOUN
fcis-22610	34	4	of	of	ADP
fcis-22610	34	5	this	this	DET
fcis-22610	34	6	section	section	NOUN
fcis-22610	34	7	,	,	PUNCT
fcis-22610	34	8	we	we	PRON
fcis-22610	34	9	demonstrate	demonstrate	VERB
fcis-22610	34	10	the	the	DET
fcis-22610	34	11	effectiveness	effectiveness	NOUN
fcis-22610	34	12	of	of	ADP
fcis-22610	34	13	the	the	DET
fcis-22610	34	14	improved	improve	VERB
fcis-22610	34	15	module	module	NOUN
fcis-22610	34	16	through	through	ADP
fcis-22610	34	17	ablation	ablation	NOUN
fcis-22610	34	18	experiments	experiment	NOUN
fcis-22610	34	19	using	use	VERB
fcis-22610	34	20	a	a	DET
fcis-22610	34	21	control	control	NOUN
fcis-22610	34	22	variable	variable	ADJ
fcis-22610	34	23	figure	figure	NOUN
fcis-22610	34	24	1	1	NUM
fcis-22610	34	25	.	.	PUNCT
fcis-22610	34	26	comparative	comparative	ADJ
fcis-22610	34	27	learning	learning	NOUN
fcis-22610	34	28	process	process	NOUN
fcis-22610	34	29	(	(	PUNCT
fcis-22610	34	30	a	a	X
fcis-22610	34	31	):	):	PUNCT
fcis-22610	34	32	the	the	DET
fcis-22610	34	33	training	training	NOUN
fcis-22610	34	34	data	datum	NOUN
fcis-22610	34	35	consists	consist	VERB
fcis-22610	34	36	of	of	ADP
fcis-22610	34	37	(	(	PUNCT
fcis-22610	34	38	image	image	NOUN
fcis-22610	34	39	,	,	PUNCT
fcis-22610	34	40	next	next	ADJ
fcis-22610	34	41	image	image	NOUN
fcis-22610	34	42	,	,	PUNCT
fcis-22610	34	43	action	action	NOUN
fcis-22610	34	44	)	)	PUNCT
fcis-22610	34	45	,	,	PUNCT
fcis-22610	34	46	the	the	DET
fcis-22610	34	47	encoder	encoder	NOUN
fcis-22610	34	48	,	,	PUNCT
fcis-22610	34	49	and	and	CCONJ
fcis-22610	34	50	forward	forward	ADJ
fcis-22610	34	51	model	model	NOUN
fcis-22610	34	52	parts	part	NOUN
fcis-22610	34	53	of	of	ADP
fcis-22610	34	54	the	the	DET
fcis-22610	34	55	model	model	NOUN
fcis-22610	34	56	we	we	PRON
fcis-22610	34	57	designed	design	VERB
fcis-22610	34	58	.	.	PUNCT
fcis-22610	35	1	the	the	DET
fcis-22610	35	2	optimized	optimize	VERB
fcis-22610	35	3	contrastive	contrastive	ADJ
fcis-22610	35	4	loss	loss	NOUN
fcis-22610	35	5	objective	objective	NOUN
fcis-22610	35	6	makes	make	VERB
fcis-22610	35	7	positive	positive	ADJ
fcis-22610	35	8	embedding	embed	VERB
fcis-22610	35	9	pairs	pair	NOUN
fcis-22610	35	10	closer	close	ADJ
fcis-22610	35	11	and	and	CCONJ
fcis-22610	35	12	negative	negative	ADJ
fcis-22610	35	13	embeddings	embedding	NOUN
fcis-22610	35	14	farther	far	ADV
fcis-22610	35	15	.	.	PUNCT
fcis-22610	36	1	(	(	PUNCT
fcis-22610	36	2	b	b	X
fcis-22610	36	3	):	):	PUNCT
fcis-22610	36	4	we	we	PRON
fcis-22610	36	5	run	run	VERB
fcis-22610	36	6	our	our	PRON
fcis-22610	36	7	model	model	NOUN
fcis-22610	36	8	by	by	ADP
fcis-22610	36	9	designing	design	VERB
fcis-22610	36	10	multiple	multiple	ADJ
fcis-22610	36	11	actions	action	NOUN
fcis-22610	36	12	,	,	PUNCT
fcis-22610	36	13	and	and	CCONJ
fcis-22610	36	14	applying	apply	VERB
fcis-22610	36	15	different	different	ADJ
fcis-22610	36	16	actions	action	NOUN
fcis-22610	36	17	.	.	PUNCT
fcis-22610	37	1	ultimately	ultimately	ADV
fcis-22610	37	2	,	,	PUNCT
fcis-22610	37	3	we	we	PRON
fcis-22610	37	4	choose	choose	VERB
fcis-22610	37	5	a	a	DET
fcis-22610	37	6	set	set	NOUN
fcis-22610	37	7	that	that	PRON
fcis-22610	37	8	is	be	AUX
fcis-22610	37	9	closer	close	ADJ
fcis-22610	37	10	to	to	ADP
fcis-22610	37	11	our	our	PRON
fcis-22610	37	12	target	target	NOUN
fcis-22610	37	13	model	model	NOUN
fcis-22610	37	14	approach	approach	NOUN
fcis-22610	37	15	.	.	PUNCT
fcis-22610	38	1	section	section	NOUN
fcis-22610	38	2	v	v	NUM
fcis-22610	38	3	summarizes	summarize	VERB
fcis-22610	38	4	our	our	PRON
fcis-22610	38	5	work	work	NOUN
fcis-22610	38	6	and	and	CCONJ
fcis-22610	38	7	future	future	ADJ
fcis-22610	38	8	research	research	NOUN
fcis-22610	38	9	prospects	prospect	NOUN
fcis-22610	38	10	.	.	PUNCT
fcis-22610	39	1	2	2	X
fcis-22610	39	2	.	.	X
fcis-22610	39	3	related	relate	VERB
fcis-22610	39	4	work	work	NOUN
fcis-22610	39	5	2.1	2.1	NUM
fcis-22610	39	6	.	.	PUNCT
fcis-22610	40	1	deformable	deformable	ADJ
fcis-22610	40	2	object	object	NOUN
fcis-22610	40	3	manipulation	manipulation	NOUN
fcis-22610	40	4	robotic	robotic	ADJ
fcis-22610	40	5	manipulation	manipulation	NOUN
fcis-22610	40	6	of	of	ADP
fcis-22610	40	7	deformable	deformable	ADJ
fcis-22610	40	8	objects	object	NOUN
fcis-22610	40	9	has	have	VERB
fcis-22610	40	10	a	a	DET
fcis-22610	40	11	rich	rich	ADJ
fcis-22610	40	12	history	history	NOUN
fcis-22610	40	13	spanning	span	VERB
fcis-22610	40	14	diverse	diverse	ADJ
fcis-22610	40	15	domains	domain	NOUN
fcis-22610	40	16	,	,	PUNCT
fcis-22610	40	17	from	from	ADP
fcis-22610	40	18	industrial	industrial	ADJ
fcis-22610	40	19	operations	operation	NOUN
fcis-22610	40	20	to	to	ADP
fcis-22610	40	21	everyday	everyday	ADJ
fcis-22610	40	22	life	life	NOUN
fcis-22610	40	23	to	to	ADP
fcis-22610	40	24	surgical	surgical	ADJ
fcis-22610	40	25	robotics	robotic	NOUN
fcis-22610	40	26	.	.	PUNCT
fcis-22610	41	1	manipulation	manipulation	NOUN
fcis-22610	41	2	of	of	ADP
fcis-22610	41	3	deformable	deformable	ADJ
fcis-22610	41	4	linear	linear	ADJ
fcis-22610	41	5	objects	object	NOUN
fcis-22610	41	6	there	there	PRON
fcis-22610	41	7	has	have	AUX
fcis-22610	41	8	been	be	AUX
fcis-22610	41	9	a	a	DET
fcis-22610	41	10	lot	lot	NOUN
fcis-22610	41	11	of	of	ADP
fcis-22610	41	12	previous	previous	ADJ
fcis-22610	41	13	work	work	NOUN
fcis-22610	41	14	in	in	ADP
fcis-22610	41	15	the	the	DET
fcis-22610	41	16	field	field	NOUN
fcis-22610	41	17	of	of	ADP
fcis-22610	41	18	robotic	robotic	ADJ
fcis-22610	41	19	manipulation	manipulation	NOUN
fcis-22610	41	20	of	of	ADP
fcis-22610	41	21	deformable	deformable	ADJ
fcis-22610	41	22	objects	object	NOUN
fcis-22610	41	23	.	.	PUNCT
fcis-22610	42	1	for	for	ADP
fcis-22610	42	2	detailed	detailed	ADJ
fcis-22610	42	3	information	information	NOUN
fcis-22610	42	4	,	,	PUNCT
fcis-22610	42	5	the	the	DET
fcis-22610	42	6	reader	reader	NOUN
fcis-22610	42	7	is	be	AUX
fcis-22610	42	8	referred	refer	VERB
fcis-22610	42	9	to	to	ADP
fcis-22610	42	10	khalil	khalil	PROPN
fcis-22610	42	11	and	and	CCONJ
fcis-22610	42	12	payeur[15	payeur[15	NOUN
fcis-22610	42	13	]	]	PUNCT
fcis-22610	42	14	,	,	PUNCT
fcis-22610	42	15	henrich	henrich	ADJ
fcis-22610	42	16	and	and	CCONJ
fcis-22610	42	17	wörn[2	wörn[2	NOUN
fcis-22610	42	18	]	]	X
fcis-22610	42	19	.	.	PUNCT
fcis-22610	43	1	the	the	DET
fcis-22610	43	2	standard	standard	ADJ
fcis-22610	43	3	approach	approach	NOUN
fcis-22610	43	4	to	to	ADP
fcis-22610	43	5	linear	linear	NOUN
fcis-22610	43	6	manipulations	manipulation	NOUN
fcis-22610	43	7	such	such	ADJ
fcis-22610	43	8	as	as	ADP
fcis-22610	43	9	ropes	rope	NOUN
fcis-22610	43	10	is	be	AUX
fcis-22610	43	11	to	to	PART
fcis-22610	43	12	use	use	VERB
fcis-22610	43	13	deformable	deformable	ADJ
fcis-22610	43	14	object	object	NOUN
fcis-22610	43	15	simulation	simulation	NOUN
fcis-22610	43	16	in	in	ADP
fcis-22610	43	17	conjunction	conjunction	NOUN
fcis-22610	43	18	with	with	ADP
fcis-22610	43	19	planning	planning	NOUN
fcis-22610	43	20	methods	method	NOUN
fcis-22610	43	21	[	[	X
fcis-22610	43	22	16	16	NUM
fcis-22610	43	23	]	]	PUNCT
fcis-22610	43	24	.	.	PUNCT
fcis-22610	44	1	past	past	ADJ
fcis-22610	44	2	work	work	NOUN
fcis-22610	44	3	in	in	ADP
fcis-22610	44	4	this	this	DET
fcis-22610	44	5	area	area	NOUN
fcis-22610	44	6	has	have	AUX
fcis-22610	44	7	focused	focus	VERB
fcis-22610	44	8	on	on	ADP
fcis-22610	44	9	simple	simple	ADJ
fcis-22610	44	10	linear	linear	ADJ
fcis-22610	44	11	deformable	deformable	ADJ
fcis-22610	44	12	objects	object	NOUN
fcis-22610	44	13	[	[	X
fcis-22610	44	14	17	17	NUM
fcis-22610	44	15	,	,	PUNCT
fcis-22610	44	16	18	18	NUM
fcis-22610	44	17	,	,	PUNCT
fcis-22610	44	18	19	19	NUM
fcis-22610	44	19	]	]	PUNCT
fcis-22610	44	20	,	,	PUNCT
fcis-22610	44	21	creating	create	VERB
fcis-22610	44	22	better	well	ADJ
fcis-22610	44	23	simulations	simulation	NOUN
fcis-22610	44	24	[	[	X
fcis-22610	44	25	20	20	NUM
fcis-22610	44	26	]	]	PUNCT
fcis-22610	44	27	,	,	PUNCT
fcis-22610	44	28	and	and	CCONJ
fcis-22610	44	29	faster	fast	ADJ
fcis-22610	44	30	planning	planning	NOUN
fcis-22610	44	31	[	[	X
fcis-22610	44	32	21	21	NUM
fcis-22610	44	33	]	]	PUNCT
fcis-22610	44	34	.	.	PUNCT
fcis-22610	45	1	rodriguez	rodriguez	NOUN
fcis-22610	45	2	et	et	PROPN
fcis-22610	45	3	al	al	PROPN
fcis-22610	45	4	.	.	PUNCT
fcis-22610	46	1	[	[	X
fcis-22610	46	2	21	21	NUM
fcis-22610	46	3	]	]	PUNCT
fcis-22610	46	4	developed	develop	VERB
fcis-22610	46	5	methods	method	NOUN
fcis-22610	46	6	for	for	ADP
fcis-22610	46	7	deformable	deformable	ADJ
fcis-22610	46	8	simulation	simulation	NOUN
fcis-22610	46	9	environments	environment	NOUN
fcis-22610	46	10	,	,	PUNCT
fcis-22610	46	11	and	and	CCONJ
fcis-22610	46	12	frank	frank	PROPN
fcis-22610	46	13	et	et	PROPN
fcis-22610	46	14	al	al	PROPN
fcis-22610	46	15	.	.	PROPN
fcis-22610	46	16	developed	develop	VERB
fcis-22610	46	17	methods	method	NOUN
fcis-22610	46	18	for	for	ADP
fcis-22610	46	19	faster	fast	ADJ
fcis-22610	46	20	planning	planning	NOUN
fcis-22610	46	21	in	in	ADP
fcis-22610	46	22	deformable	deformable	ADJ
fcis-22610	46	23	environments	environment	NOUN
fcis-22610	46	24	.	.	PUNCT
fcis-22610	47	1	but	but	CCONJ
fcis-22610	47	2	the	the	DET
fcis-22610	47	3	various	various	ADJ
fcis-22610	47	4	states	state	NOUN
fcis-22610	47	5	of	of	ADP
fcis-22610	47	6	deformable	deformable	ADJ
fcis-22610	47	7	linear	linear	ADJ
fcis-22610	47	8	objects	object	NOUN
fcis-22610	47	9	are	be	AUX
fcis-22610	47	10	difficult	difficult	ADJ
fcis-22610	47	11	to	to	PART
fcis-22610	47	12	plan	plan	VERB
fcis-22610	47	13	correctly	correctly	ADV
fcis-22610	47	14	even	even	ADV
fcis-22610	47	15	with	with	ADP
fcis-22610	47	16	high	high	ADJ
fcis-22610	47	17	computational	computational	ADJ
fcis-22610	47	18	efficiency	efficiency	NOUN
fcis-22610	47	19	.	.	PUNCT
fcis-22610	48	1	some	some	DET
fcis-22610	48	2	previous	previous	ADJ
fcis-22610	48	3	methods	method	NOUN
fcis-22610	48	4	deal	deal	VERB
fcis-22610	48	5	with	with	ADP
fcis-22610	48	6	complex	complex	ADJ
fcis-22610	48	7	dynamics	dynamic	NOUN
fcis-22610	48	8	through	through	ADP
fcis-22610	48	9	local	local	ADJ
fcis-22610	48	10	controllers	controller	NOUN
fcis-22610	48	11	,	,	PUNCT
fcis-22610	48	12	rather	rather	ADV
fcis-22610	48	13	than	than	ADP
fcis-22610	48	14	planning	plan	VERB
fcis-22610	48	15	complete	complete	ADJ
fcis-22610	48	16	complex	complex	ADJ
fcis-22610	48	17	dynamics	dynamic	NOUN
fcis-22610	48	18	focusing	focus	VERB
fcis-22610	48	19	on	on	ADP
fcis-22610	48	20	simpler	simple	ADJ
fcis-22610	48	21	approximate	approximate	ADJ
fcis-22610	48	22	programming	programming	NOUN
fcis-22610	48	23	.	.	PUNCT
fcis-22610	49	1	one	one	NUM
fcis-22610	49	2	way	way	NOUN
fcis-22610	49	3	to	to	PART
fcis-22610	49	4	use	use	VERB
fcis-22610	49	5	local	local	ADJ
fcis-22610	49	6	controllers	controller	NOUN
fcis-22610	49	7	is	be	AUX
fcis-22610	49	8	model	model	NOUN
fcis-22610	49	9	-	-	PUNCT
fcis-22610	49	10	based	base	VERB
fcis-22610	49	11	serv	serv	X
fcis-22610	49	12	serving	serve	VERB
fcis-22610	49	13	[	[	PUNCT
fcis-22610	49	14	22	22	NUM
fcis-22610	49	15	]	]	PUNCT
fcis-22610	49	16	,	,	PUNCT
fcis-22610	49	17	where	where	SCONJ
fcis-22610	49	18	the	the	DET
fcis-22610	49	19	endeffector	endeffector	NOUN
fcis-22610	49	20	is	be	AUX
fcis-22610	49	21	controlled	control	VERB
fcis-22610	49	22	to	to	ADP
fcis-22610	49	23	the	the	DET
fcis-22610	49	24	target	target	NOUN
fcis-22610	49	25	position	position	NOUN
fcis-22610	49	26	rather	rather	ADV
fcis-22610	49	27	than	than	ADP
fcis-22610	49	28	being	be	AUX
fcis-22610	49	29	explicitly	explicitly	ADV
fcis-22610	49	30	planned	plan	VERB
fcis-22610	49	31	[	[	X
fcis-22610	49	32	23	23	NUM
fcis-22610	49	33	]	]	PUNCT
fcis-22610	49	34	.	.	PUNCT
fcis-22610	50	1	however	however	ADV
fcis-22610	50	2	,	,	PUNCT
fcis-22610	50	3	since	since	SCONJ
fcis-22610	50	4	controllers	controller	NOUN
fcis-22610	50	5	are	be	AUX
fcis-22610	50	6	optimized	optimize	VERB
fcis-22610	50	7	on	on	ADP
fcis-22610	50	8	simple	simple	ADJ
fcis-22610	50	9	dynamics	dynamic	NOUN
fcis-22610	50	10	,	,	PUNCT
fcis-22610	50	11	they	they	PRON
fcis-22610	50	12	often	often	ADV
fcis-22610	50	13	get	get	AUX
fcis-22610	50	14	stuck	stuck	ADJ
fcis-22610	50	15	in	in	ADP
fcis-22610	50	16	local	local	ADJ
fcis-22610	50	17	minima	minima	NOUN
fcis-22610	50	18	for	for	ADP
fcis-22610	50	19	more	more	ADJ
fcis-22610	50	20	complex	complex	ADJ
fcis-22610	50	21	dynamics	dynamic	NOUN
fcis-22610	50	22	[	[	X
fcis-22610	50	23	24	24	NUM
fcis-22610	50	24	]	]	PUNCT
fcis-22610	50	25	.	.	PUNCT
fcis-22610	51	1	to	to	PART
fcis-22610	51	2	address	address	VERB
fcis-22610	51	3	this	this	DET
fcis-22610	51	4	model	model	NOUN
fcis-22610	51	5	-	-	PUNCT
fcis-22610	51	6	based	base	VERB
fcis-22610	51	7	dependency	dependency	NOUN
fcis-22610	51	8	,	,	PUNCT
fcis-22610	51	9	berenson	berenson	PROPN
fcis-22610	52	1	[	[	X
fcis-22610	52	2	25	25	NUM
fcis-22610	52	3	]	]	PUNCT
fcis-22610	52	4	,	,	PUNCT
fcis-22610	52	5	mcconachie	mcconachie	NOUN
fcis-22610	52	6	and	and	CCONJ
fcis-22610	52	7	berenson	berenson	PROPN
fcis-22610	53	1	[	[	X
fcis-22610	53	2	26	26	NUM
fcis-22610	53	3	]	]	PUNCT
fcis-22610	53	4	,	,	PUNCT
fcis-22610	53	5	navarro	navarro	PROPN
fcis-22610	53	6	-	-	PUNCT
fcis-22610	53	7	alarcon	alarcon	PROPN
fcis-22610	53	8	et	et	PROPN
fcis-22610	53	9	al	al	PROPN
fcis-22610	53	10	.	.	PUNCT
fcis-22610	54	1	[	[	X
fcis-22610	54	2	27	27	NUM
fcis-22610	54	3	]	]	PUNCT
fcis-22610	54	4	investigated	investigate	VERB
fcis-22610	54	5	jacobian	jacobian	ADJ
fcis-22610	54	6	approximation	approximation	NOUN
fcis-22610	54	7	controllers	controller	NOUN
fcis-22610	54	8	that	that	PRON
fcis-22610	54	9	do	do	AUX
fcis-22610	54	10	not	not	PART
fcis-22610	54	11	require	require	VERB
fcis-22610	54	12	explicit	explicit	ADJ
fcis-22610	54	13	models	model	NOUN
fcis-22610	54	14	,	,	PUNCT
fcis-22610	54	15	while	while	SCONJ
fcis-22610	54	16	jia	jia	PROPN
fcis-22610	54	17	et	et	PROPN
fcis-22610	54	18	al	al	PROPN
fcis-22610	54	19	.	.	PUNCT
fcis-22610	55	1	ren	ren	PROPN
fcis-22610	56	1	[	[	X
fcis-22610	56	2	28	28	NUM
fcis-22610	56	3	]	]	PUNCT
fcis-22610	56	4	,	,	PUNCT
fcis-22610	56	5	hu	hu	PROPN
fcis-22610	56	6	et	et	PROPN
fcis-22610	56	7	al	al	PROPN
fcis-22610	56	8	.	.	PUNCT
fcis-22610	57	1	[	[	X
fcis-22610	57	2	29	29	NUM
fcis-22610	57	3	]	]	PUNCT
fcis-22610	57	4	studied	study	VERB
fcis-22610	57	5	the	the	DET
fcis-22610	57	6	learning	learning	NOUN
fcis-22610	57	7	-	-	PUNCT
fcis-22610	57	8	based	base	VERB
fcis-22610	57	9	servo	servo	NOUN
fcis-22610	57	10	technique	technique	NOUN
fcis-22610	57	11	.	.	PUNCT
fcis-22610	58	1	however	however	ADV
fcis-22610	58	2	,	,	PUNCT
fcis-22610	58	3	since	since	SCONJ
fcis-22610	58	4	controllers	controller	NOUN
fcis-22610	58	5	are	be	AUX
fcis-22610	58	6	still	still	ADV
fcis-22610	58	7	local	local	ADJ
fcis-22610	58	8	,	,	PUNCT
fcis-22610	58	9	they	they	PRON
fcis-22610	58	10	are	be	AUX
fcis-22610	58	11	still	still	ADV
fcis-22610	58	12	prone	prone	ADJ
fcis-22610	58	13	to	to	ADP
fcis-22610	58	14	global	global	ADJ
fcis-22610	58	15	suboptimal	suboptimal	ADJ
fcis-22610	58	16	policies	policy	NOUN
fcis-22610	58	17	.	.	PUNCT
fcis-22610	59	1	to	to	PART
fcis-22610	59	2	address	address	VERB
fcis-22610	59	3	this	this	DET
fcis-22610	59	4	problem	problem	NOUN
fcis-22610	59	5	mccain	mccain	PROPN
fcis-22610	59	6	’s	’s	PART
fcis-22610	59	7	mcconachie	mcconachie	PROPN
fcis-22610	59	8	et	et	PROPN
fcis-22610	59	9	al	al	PROPN
fcis-22610	59	10	.	.	PUNCT
fcis-22610	60	1	[	[	X
fcis-22610	60	2	24	24	NUM
fcis-22610	60	3	]	]	PUNCT
fcis-22610	60	4	combined	combined	ADJ
fcis-22610	60	5	planning	planning	NOUN
fcis-22610	60	6	with	with	ADP
fcis-22610	60	7	a	a	DET
fcis-22610	60	8	local	local	ADJ
fcis-22610	60	9	controller	controller	NOUN
fcis-22610	60	10	.	.	PUNCT
fcis-22610	61	1	although	although	SCONJ
fcis-22610	61	2	this	this	DET
fcis-22610	61	3	results	result	NOUN
fcis-22610	61	4	in	in	ADP
fcis-22610	61	5	better	well	ADJ
fcis-22610	61	6	behavior	behavior	NOUN
fcis-22610	61	7	,	,	PUNCT
fcis-22610	61	8	transferring	transfer	VERB
fcis-22610	61	9	it	it	PRON
fcis-22610	61	10	to	to	ADP
fcis-22610	61	11	robots	robot	NOUN
fcis-22610	61	12	requires	require	VERB
fcis-22610	61	13	solving	solve	VERB
fcis-22610	61	14	difficult	difficult	ADJ
fcis-22610	61	15	state	state	NOUN
fcis-22610	61	16	estimation	estimation	NOUN
fcis-22610	61	17	problems	problem	NOUN
fcis-22610	61	18	[	[	X
fcis-22610	61	19	3	3	NUM
fcis-22610	61	20	,	,	PUNCT
fcis-22610	61	21	4	4	NUM
fcis-22610	61	22	]	]	PUNCT
fcis-22610	61	23	,	,	PUNCT
fcis-22610	61	24	however	however	ADV
fcis-22610	61	25	,	,	PUNCT
fcis-22610	61	26	these	these	PRON
fcis-22610	61	27	will	will	AUX
fcis-22610	61	28	have	have	VERB
fcis-22610	61	29	various	various	ADJ
fcis-22610	61	30	accuracy	accuracy	NOUN
fcis-22610	61	31	and	and	CCONJ
fcis-22610	61	32	efficiency	efficiency	NOUN
fcis-22610	61	33	issues	issue	NOUN
fcis-22610	61	34	,	,	PUNCT
fcis-22610	61	35	we	we	PRON
fcis-22610	61	36	propose	propose	VERB
fcis-22610	61	37	a	a	DET
fcis-22610	61	38	new	new	ADJ
fcis-22610	61	39	and	and	CCONJ
fcis-22610	61	40	improved	improved	ADJ
fcis-22610	61	41	training	training	NOUN
fcis-22610	61	42	potential	potential	ADJ
fcis-22610	61	43	dynamics	dynamic	NOUN
fcis-22610	61	44	model	model	NOUN
fcis-22610	61	45	based	base	VERB
fcis-22610	61	46	on	on	ADP
fcis-22610	61	47	comparative	comparative	ADJ
fcis-22610	61	48	learning	learning	NOUN
fcis-22610	61	49	.	.	PUNCT
fcis-22610	62	1	the	the	DET
fcis-22610	62	2	model	model	NOUN
fcis-22610	62	3	uses	use	VERB
fcis-22610	62	4	the	the	DET
fcis-22610	62	5	framework	framework	NOUN
fcis-22610	62	6	of	of	ADP
fcis-22610	62	7	endto	endto	NOUN
fcis-22610	62	8	-	-	PUNCT
fcis-22610	62	9	end	end	NOUN
fcis-22610	62	10	comparative	comparative	ADJ
fcis-22610	62	11	learning	learning	NOUN
fcis-22610	62	12	to	to	PART
fcis-22610	62	13	propose	propose	VERB
fcis-22610	62	14	an	an	DET
fcis-22610	62	15	information	information	NOUN
fcis-22610	62	16	encoder	encoder	NOUN
fcis-22610	62	17	model	model	NOUN
fcis-22610	62	18	,	,	PUNCT
fcis-22610	62	19	which	which	PRON
fcis-22610	62	20	improves	improve	VERB
fcis-22610	62	21	the	the	DET
fcis-22610	62	22	ability	ability	NOUN
fcis-22610	62	23	of	of	ADP
fcis-22610	62	24	feature	feature	NOUN
fcis-22610	62	25	information	information	NOUN
fcis-22610	62	26	extraction	extraction	NOUN
fcis-22610	62	27	,	,	PUNCT
fcis-22610	62	28	so	so	SCONJ
fcis-22610	62	29	as	as	SCONJ
fcis-22610	62	30	to	to	PART
fcis-22610	62	31	improve	improve	VERB
fcis-22610	62	32	the	the	DET
fcis-22610	62	33	accuracy	accuracy	NOUN
fcis-22610	62	34	of	of	ADP
fcis-22610	62	35	rope	rope	NOUN
fcis-22610	62	36	at	at	ADP
fcis-22610	62	37	all	all	DET
fcis-22610	62	38	angles	angle	NOUN
fcis-22610	62	39	.	.	PUNCT
fcis-22610	63	1	2.2	2.2	NUM
fcis-22610	63	2	.	.	PUNCT
fcis-22610	64	1	contrastive	contrastive	ADJ
fcis-22610	64	2	learning	learn	VERB
fcis-22610	64	3	the	the	DET
fcis-22610	64	4	learning	learning	NOUN
fcis-22610	64	5	of	of	ADP
fcis-22610	64	6	dynamic	dynamic	ADJ
fcis-22610	64	7	models	model	NOUN
fcis-22610	64	8	and	and	CCONJ
fcis-22610	64	9	the	the	DET
fcis-22610	64	10	extraction	extraction	NOUN
fcis-22610	64	11	of	of	ADP
fcis-22610	64	12	well	well	ADV
fcis-22610	64	13	representative	representative	ADJ
fcis-22610	64	14	information	information	NOUN
fcis-22610	64	15	remains	remain	VERB
fcis-22610	64	16	a	a	DET
fcis-22610	64	17	formidable	formidable	ADJ
fcis-22610	64	18	challenge	challenge	NOUN
fcis-22610	64	19	in	in	ADP
fcis-22610	64	20	deformable	deformable	ADJ
fcis-22610	64	21	linear	linear	ADJ
fcis-22610	64	22	object	object	NOUN
fcis-22610	64	23	manipulation	manipulation	NOUN
fcis-22610	64	24	.	.	PUNCT
fcis-22610	65	1	contrastive	contrastive	ADJ
fcis-22610	65	2	learning	learning	NOUN
fcis-22610	65	3	is	be	AUX
fcis-22610	65	4	developing	develop	VERB
fcis-22610	65	5	rapidly	rapidly	ADV
fcis-22610	65	6	,	,	PUNCT
fcis-22610	65	7	and	and	CCONJ
fcis-22610	65	8	a	a	DET
fcis-22610	65	9	lot	lot	NOUN
fcis-22610	65	10	of	of	ADP
fcis-22610	65	11	research	research	NOUN
fcis-22610	65	12	has	have	AUX
fcis-22610	65	13	been	be	AUX
fcis-22610	65	14	done	do	VERB
fcis-22610	65	15	to	to	PART
fcis-22610	65	16	better	well	ADV
fcis-22610	65	17	represent	represent	VERB
fcis-22610	65	18	data	datum	NOUN
fcis-22610	65	19	.	.	PUNCT
fcis-22610	66	1	word2vec	word2vec	PRON
fcis-22610	66	2	optimizes	optimize	VERB
fcis-22610	66	3	a	a	DET
fcis-22610	66	4	contrastive	contrastive	ADJ
fcis-22610	66	5	loss	loss	NOUN
fcis-22610	66	6	to	to	PART
fcis-22610	66	7	demonstrate	demonstrate	VERB
fcis-22610	66	8	semantic	semantic	ADJ
fcis-22610	66	9	and	and	CCONJ
fcis-22610	66	10	syntactic	syntactic	ADJ
fcis-22610	66	11	structure	structure	NOUN
fcis-22610	66	12	in	in	ADP
fcis-22610	66	13	the	the	DET
fcis-22610	66	14	latent	latent	NOUN
fcis-22610	66	15	space	space	NOUN
fcis-22610	66	16	of	of	ADP
fcis-22610	66	17	word	word	NOUN
fcis-22610	66	18	learning	learn	VERB
fcis-22610	66	19	.	.	PUNCT
fcis-22610	67	1	[	[	X
fcis-22610	67	2	30	30	NUM
fcis-22610	67	3	]	]	PUNCT
fcis-22610	67	4	showed	show	VERB
fcis-22610	67	5	that	that	SCONJ
fcis-22610	67	6	high	high	ADJ
fcis-22610	67	7	-	-	PUNCT
fcis-22610	67	8	level	level	NOUN
fcis-22610	67	9	representations	representation	NOUN
fcis-22610	67	10	for	for	ADP
fcis-22610	67	11	image	image	NOUN
fcis-22610	67	12	,	,	PUNCT
fcis-22610	67	13	video	video	NOUN
fcis-22610	67	14	and	and	CCONJ
fcis-22610	67	15	speech	speech	NOUN
fcis-22610	67	16	data	datum	NOUN
fcis-22610	67	17	can	can	AUX
fcis-22610	67	18	be	be	AUX
fcis-22610	67	19	learned	learn	VERB
fcis-22610	67	20	by	by	ADP
fcis-22610	67	21	using	use	VERB
fcis-22610	67	22	a	a	DET
fcis-22610	67	23	large	large	ADJ
fcis-22610	67	24	number	number	NOUN
fcis-22610	67	25	of	of	ADP
fcis-22610	67	26	negative	negative	ADJ
fcis-22610	67	27	samples	sample	NOUN
fcis-22610	67	28	.	.	PUNCT
fcis-22610	68	1	tian	tian	ADJ
fcis-22610	68	2	et	et	NOUN
fcis-22610	68	3	al[31	al[31	PROPN
fcis-22610	68	4	]	]	PUNCT
fcis-22610	68	5	learn	learn	VERB
fcis-22610	68	6	high	high	ADJ
fcis-22610	68	7	-	-	PUNCT
fcis-22610	68	8	level	level	NOUN
fcis-22610	68	9	representations	representation	NOUN
fcis-22610	68	10	through	through	ADP
fcis-22610	68	11	a	a	DET
fcis-22610	68	12	framework	framework	NOUN
fcis-22610	68	13	like	like	ADP
fcis-22610	68	14	contrast	contrast	NOUN
fcis-22610	68	15	loss	loss	NOUN
fcis-22610	68	16	by	by	ADP
fcis-22610	68	17	keeping	keep	VERB
fcis-22610	68	18	positive	positive	ADJ
fcis-22610	68	19	samples	sample	NOUN
fcis-22610	68	20	of	of	ADP
fcis-22610	68	21	different	different	ADJ
fcis-22610	68	22	scenes	scene	NOUN
fcis-22610	68	23	close	close	ADJ
fcis-22610	68	24	to	to	ADP
fcis-22610	68	25	each	each	DET
fcis-22610	68	26	other	other	ADJ
fcis-22610	68	27	and	and	CCONJ
fcis-22610	68	28	far	far	ADV
fcis-22610	68	29	away	away	ADV
fcis-22610	68	30	from	from	ADP
fcis-22610	68	31	other	other	ADJ
fcis-22610	68	32	scenes	scene	NOUN
fcis-22610	68	33	.	.	PUNCT
fcis-22610	69	1	the	the	DET
fcis-22610	69	2	new	new	ADJ
fcis-22610	69	3	contrastive	contrastive	ADJ
fcis-22610	69	4	learning	learning	NOUN
fcis-22610	69	5	framework	framework	NOUN
fcis-22610	69	6	simclr	simclr	NOUN
fcis-22610	69	7	[	[	X
fcis-22610	69	8	13	13	NUM
fcis-22610	69	9	]	]	PUNCT
fcis-22610	69	10	has	have	AUX
fcis-22610	69	11	made	make	VERB
fcis-22610	69	12	important	important	ADJ
fcis-22610	69	13	progress	progress	NOUN
fcis-22610	69	14	in	in	ADP
fcis-22610	69	15	feature	feature	NOUN
fcis-22610	69	16	representation	representation	NOUN
fcis-22610	69	17	,	,	PUNCT
fcis-22610	69	18	providing	provide	VERB
fcis-22610	69	19	a	a	DET
fcis-22610	69	20	new	new	ADJ
fcis-22610	69	21	method	method	NOUN
fcis-22610	69	22	for	for	ADP
fcis-22610	69	23	developing	develop	VERB
fcis-22610	69	24	flexible	flexible	ADJ
fcis-22610	69	25	linear	linear	ADJ
fcis-22610	69	26	object	object	NOUN
fcis-22610	69	27	capture	capture	NOUN
fcis-22610	69	28	.	.	PUNCT
fcis-22610	70	1	this	this	DET
fcis-22610	70	2	method	method	NOUN
fcis-22610	70	3	combines	combine	VERB
fcis-22610	70	4	different	different	ADJ
fcis-22610	70	5	samples	sample	NOUN
fcis-22610	70	6	in	in	ADP
fcis-22610	70	7	the	the	DET
fcis-22610	70	8	embedded	embed	VERB
fcis-22610	70	9	space	space	NOUN
fcis-22610	70	10	,	,	PUNCT
fcis-22610	70	11	so	so	SCONJ
fcis-22610	70	12	that	that	SCONJ
fcis-22610	70	13	the	the	DET
fcis-22610	70	14	positive	positive	ADJ
fcis-22610	70	15	samples	sample	NOUN
fcis-22610	70	16	should	should	AUX
fcis-22610	70	17	be	be	AUX
fcis-22610	70	18	as	as	ADV
fcis-22610	70	19	close	close	ADJ
fcis-22610	70	20	as	as	ADP
fcis-22610	70	21	possible	possible	ADJ
fcis-22610	70	22	and	and	CCONJ
fcis-22610	70	23	the	the	DET
fcis-22610	70	24	negative	negative	ADJ
fcis-22610	70	25	samples	sample	NOUN
fcis-22610	70	26	should	should	AUX
fcis-22610	70	27	be	be	AUX
fcis-22610	70	28	as	as	ADV
fcis-22610	70	29	far	far	ADV
fcis-22610	70	30	away	away	ADV
fcis-22610	70	31	as	as	ADP
fcis-22610	70	32	possible	possible	ADJ
fcis-22610	70	33	.	.	PUNCT
fcis-22610	71	1	in	in	ADP
fcis-22610	71	2	end	end	NOUN
fcis-22610	71	3	-	-	PUNCT
fcis-22610	71	4	to	to	ADP
fcis-22610	71	5	-	-	PUNCT
fcis-22610	71	6	end	end	NOUN
fcis-22610	71	7	learning	learning	NOUN
fcis-22610	71	8	,	,	PUNCT
fcis-22610	71	9	one	one	NUM
fcis-22610	71	10	encoder	encoder	NOUN
fcis-22610	71	11	generates	generate	VERB
fcis-22610	71	12	positive	positive	ADJ
fcis-22610	71	13	samples	sample	NOUN
fcis-22610	71	14	and	and	CCONJ
fcis-22610	71	15	the	the	DET
fcis-22610	71	16	other	other	ADJ
fcis-22610	71	17	generates	generate	VERB
fcis-22610	71	18	negative	negative	ADJ
fcis-22610	71	19	samples	sample	NOUN
fcis-22610	71	20	for	for	ADP
fcis-22610	71	21	training	training	NOUN
fcis-22610	71	22	and	and	CCONJ
fcis-22610	71	23	learning	learning	NOUN
fcis-22610	71	24	,	,	PUNCT
fcis-22610	71	25	and	and	CCONJ
fcis-22610	71	26	then	then	ADV
fcis-22610	71	27	passes	pass	VERB
fcis-22610	71	28	them	they	PRON
fcis-22610	71	29	to	to	ADP
fcis-22610	71	30	downstream	downstream	ADJ
fcis-22610	71	31	tasks	task	NOUN
fcis-22610	71	32	.	.	PUNCT
fcis-22610	72	1	this	this	DET
fcis-22610	72	2	method	method	NOUN
fcis-22610	72	3	costs	cost	VERB
fcis-22610	72	4	more	more	ADJ
fcis-22610	72	5	batch	batch	NOUN
fcis-22610	72	6	size	size	NOUN
fcis-22610	72	7	.	.	PUNCT
fcis-22610	73	1	we	we	PRON
fcis-22610	73	2	designed	design	VERB
fcis-22610	73	3	a	a	DET
fcis-22610	73	4	model	model	NOUN
fcis-22610	73	5	of	of	ADP
fcis-22610	73	6	a	a	DET
fcis-22610	73	7	single	single	ADJ
fcis-22610	73	8	encoder	encoder	NOUN
fcis-22610	73	9	,	,	PUNCT
fcis-22610	73	10	and	and	CCONJ
fcis-22610	73	11	in	in	ADP
fcis-22610	73	12	the	the	DET
fcis-22610	73	13	encoder	encoder	NOUN
fcis-22610	73	14	stage	stage	NOUN
fcis-22610	73	15	,	,	PUNCT
fcis-22610	73	16	we	we	PRON
fcis-22610	73	17	combined	combine	VERB
fcis-22610	73	18	the	the	DET
fcis-22610	73	19	residual	residual	ADJ
fcis-22610	73	20	structure	structure	NOUN
fcis-22610	73	21	to	to	PART
fcis-22610	73	22	optimize	optimize	VERB
fcis-22610	73	23	the	the	DET
fcis-22610	73	24	structure	structure	NOUN
fcis-22610	73	25	of	of	ADP
fcis-22610	73	26	each	each	DET
fcis-22610	73	27	step	step	NOUN
fcis-22610	73	28	,	,	PUNCT
fcis-22610	73	29	so	so	SCONJ
fcis-22610	73	30	that	that	SCONJ
fcis-22610	73	31	the	the	DET
fcis-22610	73	32	batch	batch	NOUN
fcis-22610	73	33	size	size	NOUN
fcis-22610	73	34	does	do	AUX
fcis-22610	73	35	not	not	PART
fcis-22610	73	36	need	need	VERB
fcis-22610	73	37	to	to	PART
fcis-22610	73	38	be	be	AUX
fcis-22610	73	39	particularly	particularly	ADV
fcis-22610	73	40	large	large	ADJ
fcis-22610	73	41	.	.	PUNCT
fcis-22610	74	1	we	we	PRON
fcis-22610	74	2	set	set	VERB
fcis-22610	74	3	it	it	PRON
fcis-22610	74	4	to	to	ADP
fcis-22610	74	5	128	128	NUM
fcis-22610	74	6	,	,	PUNCT
fcis-22610	74	7	and	and	CCONJ
fcis-22610	74	8	the	the	DET
fcis-22610	74	9	extraction	extraction	NOUN
fcis-22610	74	10	of	of	ADP
fcis-22610	74	11	feature	feature	NOUN
fcis-22610	74	12	information	information	NOUN
fcis-22610	74	13	has	have	AUX
fcis-22610	74	14	made	make	VERB
fcis-22610	74	15	great	great	ADJ
fcis-22610	74	16	progress	progress	NOUN
fcis-22610	74	17	,	,	PUNCT
fcis-22610	74	18	so	so	SCONJ
fcis-22610	74	19	that	that	SCONJ
fcis-22610	74	20	it	it	PRON
fcis-22610	74	21	can	can	AUX
fcis-22610	74	22	basically	basically	ADV
fcis-22610	74	23	reach	reach	VERB
fcis-22610	74	24	the	the	DET
fcis-22610	74	25	required	require	VERB
fcis-22610	74	26	shape	shape	NOUN
fcis-22610	74	27	in	in	ADP
fcis-22610	74	28	the	the	DET
fcis-22610	74	29	evaluation	evaluation	NOUN
fcis-22610	74	30	stage	stage	NOUN
fcis-22610	74	31	.	.	PUNCT
fcis-22610	75	1	3	3	X
fcis-22610	75	2	.	.	NUM
fcis-22610	75	3	proposed	propose	VERB
fcis-22610	75	4	method	method	NOUN
fcis-22610	75	5	in	in	ADP
fcis-22610	75	6	this	this	DET
fcis-22610	75	7	section	section	NOUN
fcis-22610	75	8	,	,	PUNCT
fcis-22610	75	9	we	we	PRON
fcis-22610	75	10	describe	describe	VERB
fcis-22610	75	11	the	the	DET
fcis-22610	75	12	framework	framework	NOUN
fcis-22610	75	13	we	we	PRON
fcis-22610	75	14	trained	train	VERB
fcis-22610	75	15	for	for	ADP
fcis-22610	75	16	39	39	NUM
fcis-22610	75	17	deformable	deformable	ADJ
fcis-22610	75	18	linear	linear	ADJ
fcis-22610	75	19	object	object	NOUN
fcis-22610	75	20	manipulation	manipulation	NOUN
fcis-22610	75	21	:	:	PUNCT
fcis-22610	75	22	forward	forward	ADV
fcis-22610	75	23	modeling	modeling	NOUN
fcis-22610	75	24	based	base	VERB
fcis-22610	75	25	on	on	ADP
fcis-22610	75	26	contrastive	contrastive	ADJ
fcis-22610	75	27	learning	learning	NOUN
fcis-22610	75	28	.	.	PUNCT
fcis-22610	76	1	in	in	ADP
fcis-22610	76	2	this	this	DET
fcis-22610	76	3	section	section	NOUN
fcis-22610	76	4	,	,	PUNCT
fcis-22610	76	5	we	we	PRON
fcis-22610	76	6	first	first	ADV
fcis-22610	76	7	discuss	discuss	VERB
fcis-22610	76	8	the	the	DET
fcis-22610	76	9	predictive	predictive	ADJ
fcis-22610	76	10	models	model	NOUN
fcis-22610	76	11	of	of	ADP
fcis-22610	76	12	contrastive	contrastive	ADJ
fcis-22610	76	13	learning	learning	NOUN
fcis-22610	76	14	,	,	PUNCT
fcis-22610	76	15	and	and	CCONJ
fcis-22610	76	16	important	important	ADJ
fcis-22610	76	17	improvements	improvement	NOUN
fcis-22610	76	18	to	to	ADP
fcis-22610	76	19	the	the	DET
fcis-22610	76	20	models	model	NOUN
fcis-22610	76	21	inspired	inspire	VERB
fcis-22610	76	22	by	by	ADP
fcis-22610	76	23	resnet	resnet	NOUN
fcis-22610	76	24	,	,	PUNCT
fcis-22610	76	25	and	and	CCONJ
fcis-22610	76	26	secondly	secondly	ADV
fcis-22610	76	27	,	,	PUNCT
fcis-22610	76	28	we	we	PRON
fcis-22610	76	29	discuss	discuss	VERB
fcis-22610	76	30	the	the	DET
fcis-22610	76	31	formalism	formalism	NOUN
fcis-22610	76	32	of	of	ADP
fcis-22610	76	33	predictive	predictive	ADJ
fcis-22610	76	34	modeling	modeling	NOUN
fcis-22610	76	35	and	and	CCONJ
fcis-22610	76	36	contrastive	contrastive	ADJ
fcis-22610	76	37	learning	learning	NOUN
fcis-22610	76	38	.	.	PUNCT
fcis-22610	77	1	for	for	ADP
fcis-22610	77	2	our	our	PRON
fcis-22610	77	3	training	training	NOUN
fcis-22610	77	4	program	program	NOUN
fcis-22610	77	5	,	,	PUNCT
fcis-22610	77	6	please	please	INTJ
fcis-22610	77	7	refer	refer	VERB
fcis-22610	77	8	to	to	PART
fcis-22610	77	9	figure	figure	VERB
fcis-22610	77	10	1	1	NUM
fcis-22610	77	11	.	.	NOUN
fcis-22610	77	12	3.1	3.1	NUM
fcis-22610	77	13	.	.	PUNCT
fcis-22610	78	1	predictive	predictive	ADJ
fcis-22610	78	2	models	model	NOUN
fcis-22610	78	3	figure	figure	VERB
fcis-22610	78	4	2	2	NUM
fcis-22610	78	5	.	.	PUNCT
fcis-22610	78	6	from	from	ADP
fcis-22610	78	7	the	the	DET
fcis-22610	78	8	initial	initial	ADJ
fcis-22610	78	9	state	state	NOUN
fcis-22610	78	10	,	,	PUNCT
fcis-22610	78	11	through	through	ADP
fcis-22610	78	12	our	our	PRON
fcis-22610	78	13	model	model	NOUN
fcis-22610	78	14	to	to	PART
fcis-22610	78	15	reach	reach	VERB
fcis-22610	78	16	different	different	ADJ
fcis-22610	78	17	target	target	NOUN
fcis-22610	78	18	states	state	NOUN
fcis-22610	78	19	,	,	PUNCT
fcis-22610	78	20	we	we	PRON
fcis-22610	78	21	select	select	VERB
fcis-22610	78	22	several	several	ADJ
fcis-22610	78	23	classical	classical	ADJ
fcis-22610	78	24	target	target	NOUN
fcis-22610	78	25	states	state	NOUN
fcis-22610	78	26	as	as	ADP
fcis-22610	78	27	levels	level	NOUN
fcis-22610	78	28	respectively	respectively	ADV
fcis-22610	78	29	.	.	PUNCT
fcis-22610	79	1	45	45	NUM
fcis-22610	79	2	°	°	NOUN
fcis-22610	79	3	.	.	PROPN
fcis-22610	79	4	90	90	NUM
fcis-22610	79	5	°	°	NUM
fcis-22610	79	6	.	.	PUNCT
fcis-22610	79	7	135	135	NUM
fcis-22610	79	8	°	°	NUM
fcis-22610	79	9	figure	figure	NOUN
fcis-22610	79	10	3	3	NUM
fcis-22610	79	11	.	.	PUNCT
fcis-22610	80	1	this	this	PRON
fcis-22610	80	2	is	be	AUX
fcis-22610	80	3	the	the	DET
fcis-22610	80	4	encoder	encoder	NOUN
fcis-22610	80	5	structure	structure	NOUN
fcis-22610	80	6	we	we	PRON
fcis-22610	80	7	designed	design	VERB
fcis-22610	80	8	,	,	PUNCT
fcis-22610	80	9	which	which	PRON
fcis-22610	80	10	contains	contain	VERB
fcis-22610	80	11	two	two	NUM
fcis-22610	80	12	residual	residual	ADJ
fcis-22610	80	13	structures	structure	NOUN
fcis-22610	80	14	.	.	PUNCT
fcis-22610	81	1	the	the	DET
fcis-22610	81	2	image	image	NOUN
fcis-22610	81	3	is	be	AUX
fcis-22610	81	4	input	input	NOUN
fcis-22610	81	5	and	and	CCONJ
fcis-22610	81	6	extracted	extract	VERB
fcis-22610	81	7	by	by	ADP
fcis-22610	81	8	the	the	DET
fcis-22610	81	9	encoder	encoder	NOUN
fcis-22610	81	10	and	and	CCONJ
fcis-22610	81	11	compressed	compress	VERB
fcis-22610	81	12	into	into	ADP
fcis-22610	81	13	the	the	DET
fcis-22610	81	14	potential	potential	ADJ
fcis-22610	81	15	space	space	NOUN
fcis-22610	81	16	.	.	PUNCT
fcis-22610	82	1	for	for	ADP
fcis-22610	82	2	the	the	DET
fcis-22610	82	3	formulation	formulation	NOUN
fcis-22610	82	4	of	of	ADP
fcis-22610	82	5	the	the	DET
fcis-22610	82	6	problem	problem	NOUN
fcis-22610	82	7	,	,	PUNCT
fcis-22610	82	8	an	an	DET
fcis-22610	82	9	environment	environment	NOUN
fcis-22610	82	10	is	be	AUX
fcis-22610	82	11	set	set	VERB
fcis-22610	82	12	where	where	SCONJ
fcis-22610	82	13	observations	observation	NOUN
fcis-22610	82	14	o	o	X
fcis-22610	83	1	∈	∈	PROPN
fcis-22610	83	2	o	o	NOUN
fcis-22610	83	3	,	,	PUNCT
fcis-22610	83	4	actions	action	VERB
fcis-22610	83	5	a	a	DET
fcis-22610	83	6	∈	∈	PROPN
fcis-22610	83	7	a	a	DET
fcis-22610	83	8	,	,	PUNCT
fcis-22610	83	9	and	and	CCONJ
fcis-22610	83	10	deterministic	deterministic	ADJ
fcis-22610	83	11	transition	transition	NOUN
fcis-22610	83	12	dynamics	dynamic	NOUN
fcis-22610	83	13	f	f	PROPN
fcis-22610	83	14	ot	ot	INTJ
fcis-22610	83	15	,	,	PUNCT
fcis-22610	83	16	at	at	ADP
fcis-22610	83	17	=	=	SYM
fcis-22610	83	18	ot	ot	INTJ
fcis-22610	83	19	1	1	NUM
fcis-22610	83	20	.	.	PUNCT
fcis-22610	84	1	this	this	PRON
fcis-22610	84	2	can	can	AUX
fcis-22610	84	3	learn	learn	VERB
fcis-22610	84	4	to	to	PART
fcis-22610	84	5	train	train	VERB
fcis-22610	84	6	latent	latent	NOUN
fcis-22610	84	7	features	feature	NOUN
fcis-22610	84	8	through	through	ADP
fcis-22610	84	9	pixel	pixel	PROPN
fcis-22610	84	10	latent	latent	PROPN
fcis-22610	84	11	space	space	NOUN
fcis-22610	84	12	to	to	PART
fcis-22610	84	13	predict	predict	VERB
fcis-22610	84	14	the	the	DET
fcis-22610	84	15	action	action	NOUN
fcis-22610	84	16	of	of	ADP
fcis-22610	84	17	the	the	DET
fcis-22610	84	18	next	next	ADJ
fcis-22610	84	19	time	time	NOUN
fcis-22610	84	20	step	step	NOUN
fcis-22610	84	21	,	,	PUNCT
fcis-22610	84	22	and	and	CCONJ
fcis-22610	84	23	obtain	obtain	VERB
fcis-22610	84	24	observation	observation	NOUN
fcis-22610	84	25	-	-	PUNCT
fcis-22610	84	26	action	action	NOUN
fcis-22610	84	27	-	-	PUNCT
fcis-22610	84	28	observation	observation	NOUN
fcis-22610	84	29	tuples	tuple	NOUN
fcis-22610	85	1	[	[	X
fcis-22610	85	2	33	33	NUM
fcis-22610	85	3	]	]	PUNCT
fcis-22610	85	4	through	through	ADP
fcis-22610	85	5	observation	observation	NOUN
fcis-22610	85	6	.	.	PUNCT
fcis-22610	86	1	when	when	SCONJ
fcis-22610	86	2	training	train	VERB
fcis-22610	86	3	a	a	DET
fcis-22610	86	4	prediction	prediction	NOUN
fcis-22610	86	5	model	model	NOUN
fcis-22610	86	6	,	,	PUNCT
fcis-22610	86	7	we	we	PRON
fcis-22610	86	8	can	can	AUX
fcis-22610	86	9	use	use	VERB
fcis-22610	86	10	it	it	PRON
fcis-22610	86	11	to	to	PART
fcis-22610	86	12	plan	plan	VERB
fcis-22610	86	13	deformable	deformable	ADJ
fcis-22610	86	14	linear	linear	ADJ
fcis-22610	86	15	objects	object	NOUN
fcis-22610	86	16	in	in	ADP
fcis-22610	86	17	different	different	ADJ
fcis-22610	86	18	states	state	NOUN
fcis-22610	86	19	to	to	PART
fcis-22610	86	20	reach	reach	VERB
fcis-22610	86	21	different	different	ADJ
fcis-22610	86	22	expected	expect	VERB
fcis-22610	86	23	target	target	NOUN
fcis-22610	86	24	states	state	NOUN
fcis-22610	86	25	.	.	PUNCT
fcis-22610	87	1	for	for	ADP
fcis-22610	87	2	example	example	NOUN
fcis-22610	87	3	,	,	PUNCT
fcis-22610	87	4	please	please	INTJ
fcis-22610	87	5	refer	refer	VERB
fcis-22610	87	6	to	to	PART
fcis-22610	87	7	figure	figure	VERB
fcis-22610	87	8	2	2	NUM
fcis-22610	87	9	for	for	ADP
fcis-22610	87	10	the	the	DET
fcis-22610	87	11	different	different	ADJ
fcis-22610	87	12	operations	operation	NOUN
fcis-22610	87	13	of	of	ADP
fcis-22610	87	14	the	the	DET
fcis-22610	87	15	rope	rope	NOUN
fcis-22610	87	16	.	.	PUNCT
fcis-22610	88	1	if	if	SCONJ
fcis-22610	88	2	the	the	DET
fcis-22610	88	3	planning	planning	NOUN
fcis-22610	88	4	is	be	AUX
fcis-22610	88	5	done	do	VERB
fcis-22610	88	6	through	through	ADP
fcis-22610	88	7	the	the	DET
fcis-22610	88	8	pixel	pixel	PROPN
fcis-22610	88	9	space	space	NOUN
fcis-22610	88	10	of	of	ADP
fcis-22610	88	11	the	the	DET
fcis-22610	88	12	picture	picture	NOUN
fcis-22610	88	13	,	,	PUNCT
fcis-22610	88	14	that	that	ADV
fcis-22610	88	15	is	is	ADV
fcis-22610	88	16	,	,	PUNCT
fcis-22610	88	17	the	the	DET
fcis-22610	88	18	pixel	pixel	PROPN
fcis-22610	88	19	value	value	NOUN
fcis-22610	88	20	of	of	ADP
fcis-22610	88	21	the	the	DET
fcis-22610	88	22	picture	picture	NOUN
fcis-22610	88	23	,	,	PUNCT
fcis-22610	88	24	there	there	PRON
fcis-22610	88	25	will	will	AUX
fcis-22610	88	26	be	be	AUX
fcis-22610	88	27	a	a	DET
fcis-22610	88	28	great	great	ADJ
fcis-22610	88	29	precision	precision	NOUN
fcis-22610	88	30	deviation	deviation	NOUN
fcis-22610	88	31	,	,	PUNCT
fcis-22610	88	32	because	because	SCONJ
fcis-22610	88	33	the	the	DET
fcis-22610	88	34	distance	distance	NOUN
fcis-22610	88	35	between	between	ADP
fcis-22610	88	36	the	the	DET
fcis-22610	88	37	pixel	pixel	PROPN
fcis-22610	88	38	values	value	NOUN
fcis-22610	88	39	of	of	ADP
fcis-22610	88	40	the	the	DET
fcis-22610	88	41	picture	picture	NOUN
fcis-22610	88	42	can	can	AUX
fcis-22610	88	43	not	not	PART
fcis-22610	88	44	have	have	VERB
fcis-22610	88	45	a	a	DET
fcis-22610	88	46	good	good	ADJ
fcis-22610	88	47	correlation	correlation	NOUN
fcis-22610	88	48	with	with	ADP
fcis-22610	88	49	the	the	DET
fcis-22610	88	50	real	real	ADJ
fcis-22610	88	51	distance	distance	NOUN
fcis-22610	88	52	.	.	PUNCT
fcis-22610	89	1	for	for	ADP
fcis-22610	89	2	example	example	NOUN
fcis-22610	89	3	,	,	PUNCT
fcis-22610	89	4	in	in	ADP
fcis-22610	89	5	a	a	DET
fcis-22610	89	6	task	task	NOUN
fcis-22610	89	7	where	where	SCONJ
fcis-22610	89	8	the	the	DET
fcis-22610	89	9	screw	screw	NOUN
fcis-22610	89	10	is	be	AUX
fcis-22610	89	11	placed	place	VERB
fcis-22610	89	12	in	in	ADP
fcis-22610	89	13	the	the	DET
fcis-22610	89	14	center	center	NOUN
fcis-22610	89	15	of	of	ADP
fcis-22610	89	16	the	the	DET
fcis-22610	89	17	box	box	NOUN
fcis-22610	89	18	,	,	PUNCT
fcis-22610	89	19	the	the	DET
fcis-22610	89	20	screw	screw	NOUN
fcis-22610	89	21	needs	need	VERB
fcis-22610	89	22	to	to	PART
fcis-22610	89	23	be	be	AUX
fcis-22610	89	24	placed	place	VERB
fcis-22610	89	25	in	in	ADP
fcis-22610	89	26	the	the	DET
fcis-22610	89	27	center	center	NOUN
fcis-22610	89	28	of	of	ADP
fcis-22610	89	29	the	the	DET
fcis-22610	89	30	hole	hole	NOUN
fcis-22610	89	31	.	.	PUNCT
fcis-22610	90	1	if	if	SCONJ
fcis-22610	90	2	the	the	DET
fcis-22610	90	3	screw	screw	NOUN
fcis-22610	90	4	is	be	AUX
fcis-22610	90	5	farther	far	ADV
fcis-22610	90	6	from	from	ADP
fcis-22610	90	7	the	the	DET
fcis-22610	90	8	center	center	NOUN
fcis-22610	90	9	of	of	ADP
fcis-22610	90	10	the	the	DET
fcis-22610	90	11	box	box	NOUN
fcis-22610	90	12	,	,	PUNCT
fcis-22610	90	13	then	then	ADV
fcis-22610	90	14	if	if	SCONJ
fcis-22610	90	15	comparing	compare	VERB
fcis-22610	90	16	in	in	ADP
fcis-22610	90	17	pixel	pixel	PROPN
fcis-22610	90	18	space	space	NOUN
fcis-22610	90	19	,	,	PUNCT
fcis-22610	90	20	the	the	DET
fcis-22610	90	21	next	next	ADJ
fcis-22610	90	22	action	action	NOUN
fcis-22610	90	23	may	may	AUX
fcis-22610	90	24	be	be	AUX
fcis-22610	90	25	the	the	DET
fcis-22610	90	26	same	same	ADJ
fcis-22610	90	27	distance	distance	NOUN
fcis-22610	90	28	from	from	ADP
fcis-22610	90	29	the	the	DET
fcis-22610	90	30	center	center	NOUN
fcis-22610	90	31	image	image	NOUN
fcis-22610	90	32	when	when	SCONJ
fcis-22610	90	33	using	use	VERB
fcis-22610	90	34	the	the	DET
fcis-22610	90	35	predictions	prediction	NOUN
fcis-22610	90	36	of	of	ADP
fcis-22610	90	37	the	the	DET
fcis-22610	90	38	vision	vision	NOUN
fcis-22610	90	39	-	-	PUNCT
fcis-22610	90	40	forward	forward	NOUN
fcis-22610	90	41	model	model	NOUN
fcis-22610	90	42	,	,	PUNCT
fcis-22610	90	43	since	since	SCONJ
fcis-22610	90	44	there	there	PRON
fcis-22610	90	45	is	be	VERB
fcis-22610	90	46	no	no	DET
fcis-22610	90	47	image	image	NOUN
fcis-22610	90	48	overlap	overlap	NOUN
fcis-22610	90	49	here	here	ADV
fcis-22610	90	50	.	.	PUNCT
fcis-22610	91	1	therefore	therefore	ADV
fcis-22610	91	2	,	,	PUNCT
fcis-22610	91	3	the	the	DET
fcis-22610	91	4	prediction	prediction	NOUN
fcis-22610	91	5	framework	framework	NOUN
fcis-22610	91	6	we	we	PRON
fcis-22610	91	7	consider	consider	VERB
fcis-22610	91	8	is	be	AUX
fcis-22610	91	9	to	to	PART
fcis-22610	91	10	compress	compress	VERB
fcis-22610	91	11	the	the	DET
fcis-22610	91	12	image	image	NOUN
fcis-22610	91	13	in	in	ADP
fcis-22610	91	14	the	the	DET
fcis-22610	91	15	latent	latent	NOUN
fcis-22610	91	16	space	space	NOUN
fcis-22610	91	17	for	for	ADP
fcis-22610	91	18	training	training	NOUN
fcis-22610	91	19	and	and	CCONJ
fcis-22610	91	20	prediction	prediction	NOUN
fcis-22610	91	21	,	,	PUNCT
fcis-22610	91	22	and	and	CCONJ
fcis-22610	91	23	then	then	ADV
fcis-22610	91	24	train	train	VERB
fcis-22610	91	25	and	and	CCONJ
fcis-22610	91	26	predict	predict	VERB
fcis-22610	91	27	through	through	ADP
fcis-22610	91	28	the	the	DET
fcis-22610	91	29	encoder	encoder	NOUN
fcis-22610	91	30	.	.	PUNCT
fcis-22610	92	1	we	we	PRON
fcis-22610	92	2	design	design	VERB
fcis-22610	92	3	a	a	DET
fcis-22610	92	4	resnet	resnet	NOUN
fcis-22610	92	5	-	-	PUNCT
fcis-22610	92	6	based	base	VERB
fcis-22610	92	7	encoder	encoder	NOUN
fcis-22610	92	8	,	,	PUNCT
fcis-22610	92	9	the	the	DET
fcis-22610	92	10	network	network	NOUN
fcis-22610	92	11	structure	structure	NOUN
fcis-22610	92	12	is	be	AUX
fcis-22610	92	13	shown	show	VERB
fcis-22610	92	14	in	in	ADP
fcis-22610	92	15	figure	figure	NOUN
fcis-22610	92	16	3	3	NUM
fcis-22610	92	17	,	,	PUNCT
fcis-22610	92	18	the	the	DET
fcis-22610	92	19	training	training	NOUN
fcis-22610	92	20	data	data	NOUN
fcis-22610	92	21	is	be	AUX
fcis-22610	92	22	embedded	embed	VERB
fcis-22610	92	23	in	in	ADP
fcis-22610	92	24	a	a	DET
fcis-22610	92	25	latent	latent	NOUN
fcis-22610	92	26	space	space	NOUN
fcis-22610	92	27	,	,	PUNCT
fcis-22610	92	28	and	and	CCONJ
fcis-22610	92	29	a	a	DET
fcis-22610	92	30	forward	forward	ADJ
fcis-22610	92	31	prediction	prediction	NOUN
fcis-22610	92	32	model	model	NOUN
fcis-22610	92	33	is	be	AUX
fcis-22610	92	34	combined	combine	VERB
fcis-22610	92	35	in	in	ADP
fcis-22610	92	36	the	the	DET
fcis-22610	92	37	latent	latent	NOUN
fcis-22610	92	38	space	space	NOUN
fcis-22610	92	39	.	.	PUNCT
fcis-22610	93	1	contrastive	contrastive	ADJ
fcis-22610	93	2	methods	method	NOUN
fcis-22610	93	3	learn	learn	VERB
fcis-22610	93	4	through	through	ADP
fcis-22610	93	5	positive	positive	ADJ
fcis-22610	93	6	and	and	CCONJ
fcis-22610	93	7	negative	negative	ADJ
fcis-22610	93	8	samples	sample	NOUN
fcis-22610	93	9	,	,	PUNCT
fcis-22610	93	10	so	so	SCONJ
fcis-22610	93	11	we	we	PRON
fcis-22610	93	12	found	find	VERB
fcis-22610	93	13	that	that	SCONJ
fcis-22610	93	14	end	end	NOUN
fcis-22610	93	15	-	-	PUNCT
fcis-22610	93	16	to	to	ADP
fcis-22610	93	17	-	-	PUNCT
fcis-22610	93	18	end	end	NOUN
fcis-22610	93	19	contrastive	contrastive	ADJ
fcis-22610	93	20	learning	learning	NOUN
fcis-22610	93	21	is	be	AUX
fcis-22610	93	22	more	more	ADV
fcis-22610	93	23	suitable	suitable	ADJ
fcis-22610	93	24	for	for	ADP
fcis-22610	93	25	learning	learn	VERB
fcis-22610	93	26	the	the	DET
fcis-22610	93	27	latent	latent	NOUN
fcis-22610	93	28	space	space	NOUN
fcis-22610	93	29	,	,	PUNCT
fcis-22610	93	30	in	in	ADP
fcis-22610	93	31	which	which	PRON
fcis-22610	93	32	the	the	DET
fcis-22610	93	33	design	design	NOUN
fcis-22610	93	34	of	of	ADP
fcis-22610	93	35	the	the	DET
fcis-22610	93	36	encoder	encoder	NOUN
fcis-22610	93	37	part	part	NOUN
fcis-22610	93	38	is	be	AUX
fcis-22610	93	39	particularly	particularly	ADV
fcis-22610	93	40	important	important	ADJ
fcis-22610	93	41	,	,	PUNCT
fcis-22610	93	42	which	which	PRON
fcis-22610	93	43	determines	determine	VERB
fcis-22610	93	44	the	the	DET
fcis-22610	93	45	efficiency	efficiency	NOUN
fcis-22610	93	46	of	of	ADP
fcis-22610	93	47	training	training	NOUN
fcis-22610	93	48	and	and	CCONJ
fcis-22610	93	49	the	the	DET
fcis-22610	93	50	success	success	NOUN
fcis-22610	93	51	of	of	ADP
fcis-22610	93	52	downstream	downstream	ADJ
fcis-22610	93	53	tasks	task	NOUN
fcis-22610	93	54	.	.	PUNCT
fcis-22610	94	1	3.2	3.2	NUM
fcis-22610	94	2	.	.	PUNCT
fcis-22610	94	3	contrastive	contrastive	ADJ
fcis-22610	94	4	learning	learning	NOUN
fcis-22610	94	5	in	in	ADP
fcis-22610	94	6	the	the	DET
fcis-22610	94	7	overall	overall	ADJ
fcis-22610	94	8	process	process	NOUN
fcis-22610	94	9	of	of	ADP
fcis-22610	94	10	contrastive	contrastive	ADJ
fcis-22610	94	11	learning	learning	NOUN
fcis-22610	94	12	,	,	PUNCT
fcis-22610	94	13	first	first	ADV
fcis-22610	94	14	collect	collect	VERB
fcis-22610	94	15	data	datum	NOUN
fcis-22610	94	16	and	and	CCONJ
fcis-22610	94	17	make	make	VERB
fcis-22610	94	18	data	datum	NOUN
fcis-22610	94	19	sets	set	NOUN
fcis-22610	94	20	.	.	PUNCT
fcis-22610	95	1	then	then	ADV
fcis-22610	95	2	press	press	VERB
fcis-22610	95	3	the	the	DET
fcis-22610	95	4	data	datum	NOUN
fcis-22610	95	5	into	into	ADP
fcis-22610	95	6	the	the	DET
fcis-22610	95	7	latent	latent	NOUN
fcis-22610	95	8	space	space	NOUN
fcis-22610	95	9	.	.	PUNCT
fcis-22610	96	1	this	this	PRON
fcis-22610	96	2	is	be	AUX
fcis-22610	96	3	the	the	DET
fcis-22610	96	4	most	most	ADV
fcis-22610	96	5	important	important	ADJ
fcis-22610	96	6	step	step	NOUN
fcis-22610	96	7	.	.	PUNCT
fcis-22610	97	1	only	only	ADV
fcis-22610	97	2	by	by	ADP
fcis-22610	97	3	designing	design	VERB
fcis-22610	97	4	the	the	DET
fcis-22610	97	5	encoder	encoder	NOUN
fcis-22610	97	6	well	well	ADV
fcis-22610	97	7	can	can	AUX
fcis-22610	97	8	the	the	DET
fcis-22610	97	9	overall	overall	ADJ
fcis-22610	97	10	efficiency	efficiency	NOUN
fcis-22610	97	11	and	and	CCONJ
fcis-22610	97	12	data	data	NOUN
fcis-22610	97	13	compression	compression	NOUN
fcis-22610	97	14	quality	quality	NOUN
fcis-22610	97	15	be	be	AUX
fcis-22610	97	16	guaranteed	guarantee	VERB
fcis-22610	97	17	,	,	PUNCT
fcis-22610	97	18	as	as	SCONJ
fcis-22610	97	19	shown	show	VERB
fcis-22610	97	20	in	in	ADP
fcis-22610	97	21	figure	figure	NOUN
fcis-22610	97	22	3	3	NUM
fcis-22610	97	23	.	.	PUNCT
fcis-22610	98	1	finally	finally	ADV
fcis-22610	98	2	,	,	PUNCT
fcis-22610	98	3	the	the	DET
fcis-22610	98	4	data	datum	NOUN
fcis-22610	98	5	40	40	NUM
fcis-22610	98	6	of	of	ADP
fcis-22610	98	7	the	the	DET
fcis-22610	98	8	positive	positive	ADJ
fcis-22610	98	9	and	and	CCONJ
fcis-22610	98	10	negative	negative	ADJ
fcis-22610	98	11	samples	sample	NOUN
fcis-22610	98	12	are	be	AUX
fcis-22610	98	13	compared	compare	VERB
fcis-22610	98	14	.	.	PUNCT
fcis-22610	99	1	the	the	DET
fcis-22610	99	2	gradient	gradient	ADJ
fcis-22610	99	3	calculation	calculation	NOUN
fcis-22610	99	4	is	be	AUX
fcis-22610	99	5	performed	perform	VERB
fcis-22610	99	6	by	by	ADP
fcis-22610	99	7	comparing	compare	VERB
fcis-22610	99	8	the	the	DET
fcis-22610	99	9	loss	loss	NOUN
fcis-22610	99	10	function	function	NOUN
fcis-22610	99	11	so	so	SCONJ
fcis-22610	99	12	that	that	SCONJ
fcis-22610	99	13	the	the	DET
fcis-22610	99	14	embedding	embedding	NOUN
fcis-22610	99	15	of	of	ADP
fcis-22610	99	16	positive	positive	ADJ
fcis-22610	99	17	samples	sample	NOUN
fcis-22610	99	18	is	be	AUX
fcis-22610	99	19	closer	close	ADJ
fcis-22610	99	20	,	,	PUNCT
fcis-22610	99	21	and	and	CCONJ
fcis-22610	99	22	the	the	DET
fcis-22610	99	23	embedding	embedding	NOUN
fcis-22610	99	24	of	of	ADP
fcis-22610	99	25	negative	negative	ADJ
fcis-22610	99	26	samples	sample	NOUN
fcis-22610	99	27	is	be	AUX
fcis-22610	99	28	farther	farth	ADJ
fcis-22610	99	29	.	.	PUNCT
fcis-22610	100	1	we	we	PRON
fcis-22610	100	2	design	design	VERB
fcis-22610	100	3	the	the	DET
fcis-22610	100	4	encoder	encoder	NOUN
fcis-22610	100	5	assuming	assume	VERB
fcis-22610	100	6	gθ	gθ	PROPN
fcis-22610	100	7	(	(	PUNCT
fcis-22610	100	8	ot	ot	INTJ
fcis-22610	100	9	)	)	PUNCT
fcis-22610	100	10	=	=	SYM
fcis-22610	100	11	zt	zt	PROPN
fcis-22610	100	12	and	and	CCONJ
fcis-22610	100	13	the	the	DET
fcis-22610	100	14	forward	forward	ADJ
fcis-22610	100	15	model	model	PROPN
fcis-22610	100	16	fφ(zt	fφ(zt	PROPN
fcis-22610	100	17	,	,	PUNCT
fcis-22610	100	18	at	at	ADP
fcis-22610	100	19	)	)	PUNCT
fcis-22610	100	20	≈zt	≈zt	ADP
fcis-22610	100	21	1	1	NUM
fcis-22610	100	22	,	,	PUNCT
fcis-22610	100	23	using	use	VERB
fcis-22610	100	24	the	the	DET
fcis-22610	100	25	infonce	infonce	NOUN
fcis-22610	100	26	contrastive	contrastive	ADJ
fcis-22610	100	27	loss	loss	NOUN
fcis-22610	100	28	described	describe	VERB
fcis-22610	100	29	by	by	ADP
fcis-22610	100	30	oord	oord	PROPN
fcis-22610	100	31	et	et	PROPN
fcis-22610	100	32	al	al	PROPN
fcis-22610	101	1	[	[	X
fcis-22610	101	2	37	37	NUM
fcis-22610	101	3	]	]	PUNCT
fcis-22610	101	4	infonce	infonce	NOUN
fcis-22610	101	5	contrastive	contrastive	ADJ
fcis-22610	101	6	loss	loss	NOUN
fcis-22610	101	7	lq	lq	NOUN
fcis-22610	101	8	(	(	PUNCT
fcis-22610	101	9	1	1	NUM
fcis-22610	101	10	)	)	PUNCT
fcis-22610	101	11	q	q	PUNCT
fcis-22610	101	12	represents	represent	VERB
fcis-22610	101	13	the	the	DET
fcis-22610	101	14	data	datum	NOUN
fcis-22610	101	15	to	to	PART
fcis-22610	101	16	be	be	AUX
fcis-22610	101	17	checked	check	VERB
fcis-22610	101	18	,	,	PUNCT
fcis-22610	101	19	and	and	CCONJ
fcis-22610	101	20	k	k	PROPN
fcis-22610	101	21	represents	represent	VERB
fcis-22610	101	22	the	the	DET
fcis-22610	101	23	sample	sample	NOUN
fcis-22610	101	24	similar	similar	ADJ
fcis-22610	101	25	to	to	AUX
fcis-22610	101	26	q.	q.	VERB
fcis-22610	101	27	the	the	DET
fcis-22610	101	28	numerator	numerator	NOUN
fcis-22610	101	29	is	be	AUX
fcis-22610	101	30	the	the	DET
fcis-22610	101	31	similarity	similarity	NOUN
fcis-22610	101	32	of	of	ADP
fcis-22610	101	33	positive	positive	ADJ
fcis-22610	101	34	samples	sample	NOUN
fcis-22610	101	35	,	,	PUNCT
fcis-22610	101	36	and	and	CCONJ
fcis-22610	101	37	the	the	DET
fcis-22610	101	38	denominator	denominator	NOUN
fcis-22610	101	39	is	be	AUX
fcis-22610	101	40	the	the	DET
fcis-22610	101	41	similarity	similarity	NOUN
fcis-22610	101	42	of	of	ADP
fcis-22610	101	43	positive	positive	ADJ
fcis-22610	101	44	samples	sample	NOUN
fcis-22610	101	45	and	and	CCONJ
fcis-22610	101	46	all	all	DET
fcis-22610	101	47	negative	negative	ADJ
fcis-22610	101	48	samples	sample	NOUN
fcis-22610	101	49	.	.	PUNCT
fcis-22610	102	1	the	the	DET
fcis-22610	102	2	minimized	minimized	ADJ
fcis-22610	102	3	infonceloss	infonceloss	NOUN
fcis-22610	102	4	is	be	AUX
fcis-22610	102	5	to	to	PART
fcis-22610	102	6	maximize	maximize	VERB
fcis-22610	102	7	the	the	DET
fcis-22610	102	8	numerator	numerator	NOUN
fcis-22610	102	9	and	and	CCONJ
fcis-22610	102	10	minimize	minimize	VERB
fcis-22610	102	11	the	the	DET
fcis-22610	102	12	denominator	denominator	NOUN
fcis-22610	102	13	,	,	PUNCT
fcis-22610	102	14	that	that	ADV
fcis-22610	102	15	is	is	ADV
fcis-22610	102	16	,	,	PUNCT
fcis-22610	102	17	to	to	PART
fcis-22610	102	18	minimize	minimize	VERB
fcis-22610	102	19	the	the	DET
fcis-22610	102	20	similarity	similarity	NOUN
fcis-22610	102	21	of	of	ADP
fcis-22610	102	22	positive	positive	ADJ
fcis-22610	102	23	samples	sample	NOUN
fcis-22610	102	24	and	and	CCONJ
fcis-22610	102	25	the	the	DET
fcis-22610	102	26	similarity	similarity	NOUN
fcis-22610	102	27	of	of	ADP
fcis-22610	102	28	negative	negative	ADJ
fcis-22610	102	29	samples	sample	NOUN
fcis-22610	102	30	.	.	PUNCT
fcis-22610	103	1	then	then	ADV
fcis-22610	103	2	we	we	PRON
fcis-22610	103	3	use	use	VERB
fcis-22610	103	4	the	the	DET
fcis-22610	103	5	loss	loss	NOUN
fcis-22610	103	6	proposed	propose	VERB
fcis-22610	103	7	by	by	ADP
fcis-22610	103	8	wilson	wilson	PROPN
fcis-22610	103	9	yan	yan	PROPN
fcis-22610	104	1	[	[	X
fcis-22610	104	2	12	12	NUM
fcis-22610	104	3	]	]	PUNCT
fcis-22610	104	4	et	et	PROPN
fcis-22610	104	5	al	al	PROPN
fcis-22610	104	6	.	.	PUNCT
fcis-22610	105	1	l	l	PROPN
fcis-22610	105	2	ed	ed	NOUN
fcis-22610	105	3	log	log	NOUN
fcis-22610	105	4	(	(	PUNCT
fcis-22610	105	5	2	2	NUM
fcis-22610	105	6	)	)	PUNCT
fcis-22610	105	7	on	on	ADP
fcis-22610	105	8	this	this	DET
fcis-22610	105	9	basis	basis	NOUN
fcis-22610	105	10	,	,	PUNCT
fcis-22610	105	11	where	where	SCONJ
fcis-22610	105	12	h	h	NOUN
fcis-22610	105	13	is	be	AUX
fcis-22610	105	14	the	the	DET
fcis-22610	105	15	similarity	similarity	NOUN
fcis-22610	105	16	function	function	NOUN
fcis-22610	105	17	between	between	ADP
fcis-22610	105	18	the	the	DET
fcis-22610	105	19	computed	compute	VERB
fcis-22610	105	20	embeddings	embedding	NOUN
fcis-22610	105	21	,	,	PUNCT
fcis-22610	105	22	ẑt+1	ẑt+1	PROPN
fcis-22610	105	23	represents	represent	VERB
fcis-22610	105	24	the	the	DET
fcis-22610	105	25	next	next	ADJ
fcis-22610	105	26	state	state	NOUN
fcis-22610	105	27	of	of	ADP
fcis-22610	105	28	the	the	DET
fcis-22610	105	29	negative	negative	ADJ
fcis-22610	105	30	sample	sample	NOUN
fcis-22610	105	31	,	,	PUNCT
fcis-22610	105	32	and	and	CCONJ
fcis-22610	105	33	k	k	PROPN
fcis-22610	105	34	represents	represent	VERB
fcis-22610	105	35	that	that	SCONJ
fcis-22610	105	36	there	there	PRON
fcis-22610	105	37	are	be	VERB
fcis-22610	105	38	k	k	PRON
fcis-22610	105	39	such	such	ADJ
fcis-22610	105	40	negative	negative	ADJ
fcis-22610	105	41	samples	sample	NOUN
fcis-22610	105	42	.	.	PUNCT
fcis-22610	106	1	the	the	DET
fcis-22610	106	2	essence	essence	NOUN
fcis-22610	106	3	of	of	ADP
fcis-22610	106	4	contrastive	contrastive	ADJ
fcis-22610	106	5	learning	learning	NOUN
fcis-22610	106	6	is	be	AUX
fcis-22610	106	7	to	to	PART
fcis-22610	106	8	separate	separate	VERB
fcis-22610	106	9	the	the	DET
fcis-22610	106	10	positive	positive	ADJ
fcis-22610	106	11	and	and	CCONJ
fcis-22610	106	12	negative	negative	ADJ
fcis-22610	106	13	samples	sample	NOUN
fcis-22610	106	14	,	,	PUNCT
fcis-22610	106	15	the	the	DET
fcis-22610	106	16	positive	positive	ADJ
fcis-22610	106	17	samples	sample	NOUN
fcis-22610	106	18	are	be	AUX
fcis-22610	106	19	together	together	ADV
fcis-22610	106	20	,	,	PUNCT
fcis-22610	106	21	and	and	CCONJ
fcis-22610	106	22	the	the	DET
fcis-22610	106	23	negative	negative	ADJ
fcis-22610	106	24	samples	sample	NOUN
fcis-22610	106	25	are	be	AUX
fcis-22610	106	26	further	far	ADV
fcis-22610	106	27	separated	separate	VERB
fcis-22610	106	28	,	,	PUNCT
fcis-22610	106	29	as	as	SCONJ
fcis-22610	106	30	shown	show	VERB
fcis-22610	106	31	in	in	ADP
fcis-22610	106	32	figure	figure	NOUN
fcis-22610	106	33	1	1	NUM
fcis-22610	106	34	.	.	PUNCT
fcis-22610	107	1	then	then	ADV
fcis-22610	107	2	we	we	PRON
fcis-22610	107	3	need	need	VERB
fcis-22610	107	4	to	to	PART
fcis-22610	107	5	learn	learn	VERB
fcis-22610	107	6	a	a	DET
fcis-22610	107	7	minimal	minimal	ADJ
fcis-22610	107	8	forward	forward	ADJ
fcis-22610	107	9	model	model	NOUN
fcis-22610	107	10	‖fφ	‖fφ	PROPN
fcis-22610	107	11	zt	zt	PROPN
fcis-22610	107	12	,	,	PUNCT
fcis-22610	107	13	at	at	ADP
fcis-22610	107	14	zt	zt	PROPN
fcis-22610	107	15	1‖(3	1‖(3	NUM
fcis-22610	107	16	)	)	PUNCT
fcis-22610	107	17	the	the	DET
fcis-22610	107	18	similar	similar	ADJ
fcis-22610	107	19	function	function	NOUN
fcis-22610	107	20	used	use	VERB
fcis-22610	107	21	is	be	AUX
fcis-22610	107	22	z1	z1	ADJ
fcis-22610	107	23	,	,	PUNCT
fcis-22610	107	24	z2	z2	NUM
fcis-22610	107	25	exp	exp	NOUN
fcis-22610	107	26	‖λ1	‖λ1	PROPN
fcis-22610	107	27	z2	z2	PROPN
fcis-22610	107	28	‖2	‖2	NOUN
fcis-22610	107	29	(	(	PUNCT
fcis-22610	107	30	4	4	NUM
fcis-22610	107	31	)	)	PUNCT
fcis-22610	107	32	after	after	ADP
fcis-22610	107	33	learning	learn	VERB
fcis-22610	107	34	the	the	DET
fcis-22610	107	35	encoder	encoder	NOUN
fcis-22610	107	36	we	we	PRON
fcis-22610	107	37	enter	enter	VERB
fcis-22610	107	38	the	the	DET
fcis-22610	107	39	action	action	NOUN
fcis-22610	107	40	stage	stage	NOUN
fcis-22610	107	41	of	of	ADP
fcis-22610	107	42	the	the	DET
fcis-22610	107	43	objective	objective	ADJ
fcis-22610	107	44	function	function	NOUN
fcis-22610	107	45	,	,	PUNCT
fcis-22610	107	46	that	that	ADV
fcis-22610	107	47	is	is	ADV
fcis-22610	107	48	,	,	PUNCT
fcis-22610	107	49	the	the	DET
fcis-22610	107	50	realization	realization	NOUN
fcis-22610	107	51	of	of	ADP
fcis-22610	107	52	the	the	DET
fcis-22610	107	53	downstream	downstream	ADJ
fcis-22610	107	54	task	task	NOUN
fcis-22610	107	55	.	.	PUNCT
fcis-22610	108	1	here	here	ADV
fcis-22610	108	2	we	we	PRON
fcis-22610	108	3	also	also	ADV
fcis-22610	108	4	need	need	VERB
fcis-22610	108	5	to	to	PART
fcis-22610	108	6	use	use	VERB
fcis-22610	108	7	a	a	DET
fcis-22610	108	8	simple	simple	ADJ
fcis-22610	108	9	model	model	NOUN
fcis-22610	108	10	predictive	predictive	PROPN
fcis-22610	108	11	control	control	PROPN
fcis-22610	108	12	(	(	PUNCT
fcis-22610	108	13	mpc	mpc	NOUN
fcis-22610	108	14	)	)	PUNCT
fcis-22610	108	15	,	,	PUNCT
fcis-22610	108	16	according	accord	VERB
fcis-22610	108	17	to	to	ADP
fcis-22610	108	18	the	the	DET
fcis-22610	108	19	type	type	NOUN
fcis-22610	108	20	of	of	ADP
fcis-22610	108	21	our	our	PRON
fcis-22610	108	22	action	action	NOUN
fcis-22610	108	23	select	select	ADJ
fcis-22610	108	24	azt	azt	PROPN
fcis-22610	108	25	forward	forward	ADJ
fcis-22610	108	26	model	model	NOUN
fcis-22610	108	27	to	to	PART
fcis-22610	108	28	run	run	VERB
fcis-22610	108	29	,	,	PUNCT
fcis-22610	108	30	and	and	CCONJ
fcis-22610	108	31	select	select	VERB
fcis-22610	108	32	the	the	DET
fcis-22610	108	33	next	next	ADJ
fcis-22610	108	34	action	action	NOUN
fcis-22610	108	35	that	that	PRON
fcis-22610	108	36	is	be	AUX
fcis-22610	108	37	closest	close	ADJ
fcis-22610	108	38	to	to	ADP
fcis-22610	108	39	the	the	DET
fcis-22610	108	40	target	target	NOUN
fcis-22610	108	41	within	within	ADP
fcis-22610	108	42	the	the	DET
fcis-22610	108	43	distance	distance	NOUN
fcis-22610	108	44	of	of	ADP
fcis-22610	108	45	(	(	PUNCT
fcis-22610	108	46	4	4	X
fcis-22610	108	47	)	)	PUNCT
fcis-22610	108	48	the	the	DET
fcis-22610	108	49	similarity	similarity	NOUN
fcis-22610	108	50	function	function	NOUN
fcis-22610	108	51	,	,	PUNCT
fcis-22610	108	52	which	which	PRON
fcis-22610	108	53	also	also	ADV
fcis-22610	108	54	completes	complete	VERB
fcis-22610	108	55	the	the	DET
fcis-22610	108	56	operation	operation	NOUN
fcis-22610	108	57	of	of	ADP
fcis-22610	108	58	the	the	DET
fcis-22610	108	59	deformable	deformable	ADJ
fcis-22610	108	60	linear	linear	ADJ
fcis-22610	108	61	object	object	NOUN
fcis-22610	108	62	based	base	VERB
fcis-22610	108	63	on	on	ADP
fcis-22610	108	64	contrastive	contrastive	ADJ
fcis-22610	108	65	learning	learning	NOUN
fcis-22610	108	66	.	.	PUNCT
fcis-22610	109	1	in	in	ADP
fcis-22610	109	2	the	the	DET
fcis-22610	109	3	feature	feature	NOUN
fcis-22610	109	4	extraction	extraction	NOUN
fcis-22610	109	5	,	,	PUNCT
fcis-22610	109	6	the	the	DET
fcis-22610	109	7	encoder	encoder	NOUN
fcis-22610	109	8	that	that	PRON
fcis-22610	109	9	compresses	compress	VERB
fcis-22610	109	10	the	the	DET
fcis-22610	109	11	data	datum	NOUN
fcis-22610	109	12	into	into	ADP
fcis-22610	109	13	the	the	DET
fcis-22610	109	14	latent	latent	NOUN
fcis-22610	109	15	space	space	NOUN
fcis-22610	109	16	is	be	AUX
fcis-22610	109	17	an	an	DET
fcis-22610	109	18	important	important	ADJ
fcis-22610	109	19	link	link	NOUN
fcis-22610	109	20	between	between	ADP
fcis-22610	109	21	the	the	DET
fcis-22610	109	22	previous	previous	ADJ
fcis-22610	109	23	and	and	CCONJ
fcis-22610	109	24	the	the	DET
fcis-22610	109	25	next	next	ADJ
fcis-22610	109	26	.	.	PUNCT
fcis-22610	110	1	3.3	3.3	NUM
fcis-22610	110	2	.	.	PUNCT
fcis-22610	110	3	encoder	encoder	NOUN
fcis-22610	110	4	we	we	PRON
fcis-22610	110	5	add	add	VERB
fcis-22610	110	6	domain	domain	NOUN
fcis-22610	110	7	randomization	randomization	NOUN
fcis-22610	110	8	to	to	ADP
fcis-22610	110	9	data	data	NOUN
fcis-22610	110	10	processing	processing	NOUN
fcis-22610	110	11	,	,	PUNCT
fcis-22610	110	12	generate	generate	VERB
fcis-22610	110	13	a	a	DET
fcis-22610	110	14	large	large	ADJ
fcis-22610	110	15	number	number	NOUN
fcis-22610	110	16	of	of	ADP
fcis-22610	110	17	different	different	ADJ
fcis-22610	110	18	simulation	simulation	NOUN
fcis-22610	110	19	scenes	scene	NOUN
fcis-22610	110	20	,	,	PUNCT
fcis-22610	110	21	extract	extract	VERB
fcis-22610	110	22	better	well	ADJ
fcis-22610	110	23	feature	feature	NOUN
fcis-22610	110	24	information	information	NOUN
fcis-22610	110	25	,	,	PUNCT
fcis-22610	110	26	and	and	CCONJ
fcis-22610	110	27	make	make	VERB
fcis-22610	110	28	the	the	DET
fcis-22610	110	29	network	network	NOUN
fcis-22610	110	30	adapt	adapt	VERB
fcis-22610	110	31	to	to	ADP
fcis-22610	110	32	different	different	ADJ
fcis-22610	110	33	domains	domain	NOUN
fcis-22610	110	34	.	.	PUNCT
fcis-22610	111	1	in	in	ADP
fcis-22610	111	2	contrast	contrast	NOUN
fcis-22610	111	3	learning	learning	NOUN
fcis-22610	111	4	,	,	PUNCT
fcis-22610	111	5	the	the	DET
fcis-22610	111	6	most	most	ADV
fcis-22610	111	7	important	important	ADJ
fcis-22610	111	8	part	part	NOUN
fcis-22610	111	9	is	be	AUX
fcis-22610	111	10	feature	feature	NOUN
fcis-22610	111	11	extraction	extraction	NOUN
fcis-22610	111	12	.	.	PUNCT
fcis-22610	112	1	only	only	ADV
fcis-22610	112	2	good	good	ADJ
fcis-22610	112	3	enough	enough	ADJ
fcis-22610	112	4	feature	feature	NOUN
fcis-22610	112	5	information	information	NOUN
fcis-22610	112	6	can	can	AUX
fcis-22610	112	7	help	help	VERB
fcis-22610	112	8	subsequent	subsequent	ADJ
fcis-22610	112	9	tasks	task	NOUN
fcis-22610	112	10	.	.	PUNCT
fcis-22610	113	1	in	in	ADP
fcis-22610	113	2	the	the	DET
fcis-22610	113	3	network	network	NOUN
fcis-22610	113	4	proposed	propose	VERB
fcis-22610	113	5	by	by	ADP
fcis-22610	113	6	wilson	wilson	PROPN
fcis-22610	113	7	yan	yan	PROPN
fcis-22610	114	1	[	[	X
fcis-22610	114	2	12	12	NUM
fcis-22610	114	3	]	]	X
fcis-22610	114	4	,	,	PUNCT
fcis-22610	114	5	only	only	ADV
fcis-22610	114	6	six	six	NUM
fcis-22610	114	7	layers	layer	NOUN
fcis-22610	114	8	are	be	AUX
fcis-22610	114	9	implemented	implement	VERB
fcis-22610	114	10	.	.	PUNCT
fcis-22610	115	1	simple	simple	ADJ
fcis-22610	115	2	convolution	convolution	NOUN
fcis-22610	115	3	network	network	NOUN
fcis-22610	115	4	has	have	VERB
fcis-22610	115	5	low	low	ADJ
fcis-22610	115	6	feature	feature	NOUN
fcis-22610	115	7	dimension	dimension	NOUN
fcis-22610	115	8	and	and	CCONJ
fcis-22610	115	9	strong	strong	ADJ
fcis-22610	115	10	local	local	ADJ
fcis-22610	115	11	information	information	NOUN
fcis-22610	115	12	,	,	PUNCT
fcis-22610	115	13	but	but	CCONJ
fcis-22610	115	14	it	it	PRON
fcis-22610	115	15	lacks	lack	VERB
fcis-22610	115	16	the	the	DET
fcis-22610	115	17	diversity	diversity	NOUN
fcis-22610	115	18	of	of	ADP
fcis-22610	115	19	feature	feature	NOUN
fcis-22610	115	20	information	information	NOUN
fcis-22610	115	21	.	.	PUNCT
fcis-22610	116	1	however	however	ADV
fcis-22610	116	2	,	,	PUNCT
fcis-22610	116	3	the	the	DET
fcis-22610	116	4	network	network	NOUN
fcis-22610	116	5	with	with	ADP
fcis-22610	116	6	too	too	ADV
fcis-22610	116	7	deep	deep	ADJ
fcis-22610	116	8	depth	depth	NOUN
fcis-22610	116	9	is	be	AUX
fcis-22610	116	10	easy	easy	ADJ
fcis-22610	116	11	to	to	PART
fcis-22610	116	12	lead	lead	VERB
fcis-22610	116	13	to	to	ADP
fcis-22610	116	14	gradient	gradient	ADJ
fcis-22610	116	15	dispersion	dispersion	NOUN
fcis-22610	116	16	,	,	PUNCT
fcis-22610	116	17	while	while	SCONJ
fcis-22610	116	18	the	the	DET
fcis-22610	116	19	simple	simple	ADJ
fcis-22610	116	20	depth	depth	NOUN
fcis-22610	116	21	network	network	NOUN
fcis-22610	116	22	is	be	AUX
fcis-22610	116	23	more	more	ADV
fcis-22610	116	24	prone	prone	ADJ
fcis-22610	116	25	to	to	PART
fcis-22610	116	26	feature	feature	NOUN
fcis-22610	116	27	redundancy	redundancy	NOUN
fcis-22610	116	28	,	,	PUNCT
fcis-22610	116	29	and	and	CCONJ
fcis-22610	116	30	only	only	ADV
fcis-22610	116	31	a	a	DET
fcis-22610	116	32	small	small	ADJ
fcis-22610	116	33	part	part	NOUN
fcis-22610	116	34	of	of	ADP
fcis-22610	116	35	image	image	NOUN
fcis-22610	116	36	features	feature	NOUN
fcis-22610	116	37	is	be	AUX
fcis-22610	116	38	extracted	extract	VERB
fcis-22610	116	39	.	.	PUNCT
fcis-22610	117	1	the	the	DET
fcis-22610	117	2	result	result	NOUN
fcis-22610	117	3	of	of	ADP
fcis-22610	117	4	adding	add	VERB
fcis-22610	117	5	the	the	DET
fcis-22610	117	6	remaining	remain	VERB
fcis-22610	117	7	network	network	NOUN
fcis-22610	117	8	structure	structure	NOUN
fcis-22610	117	9	has	have	AUX
fcis-22610	117	10	changed	change	VERB
fcis-22610	117	11	significantly	significantly	ADV
fcis-22610	117	12	.	.	PUNCT
fcis-22610	118	1	we	we	PRON
fcis-22610	118	2	designed	design	VERB
fcis-22610	118	3	a	a	DET
fcis-22610	118	4	new	new	ADJ
fcis-22610	118	5	encoder	encoder	NOUN
fcis-22610	118	6	structure	structure	NOUN
fcis-22610	118	7	,	,	PUNCT
fcis-22610	118	8	as	as	SCONJ
fcis-22610	118	9	shown	show	VERB
fcis-22610	118	10	in	in	ADP
fcis-22610	118	11	figure	figure	NOUN
fcis-22610	118	12	3	3	NUM
fcis-22610	118	13	.	.	PUNCT
fcis-22610	119	1	it	it	PRON
fcis-22610	119	2	is	be	AUX
fcis-22610	119	3	mainly	mainly	ADV
fcis-22610	119	4	composed	compose	VERB
fcis-22610	119	5	of	of	ADP
fcis-22610	119	6	two	two	NUM
fcis-22610	119	7	residual	residual	ADJ
fcis-22610	119	8	structures	structure	NOUN
fcis-22610	119	9	:	:	PUNCT
fcis-22610	119	10	restnetbasicblock	restnetbasicblock	NOUN
fcis-22610	119	11	and	and	CCONJ
fcis-22610	119	12	restnetdownblock	restnetdownblock	NOUN
fcis-22610	119	13	.	.	PUNCT
fcis-22610	120	1	the	the	DET
fcis-22610	120	2	difference	difference	NOUN
fcis-22610	120	3	between	between	ADP
fcis-22610	120	4	the	the	DET
fcis-22610	120	5	two	two	NUM
fcis-22610	120	6	remaining	remain	VERB
fcis-22610	120	7	structures	structure	NOUN
fcis-22610	120	8	is	be	AUX
fcis-22610	120	9	that	that	SCONJ
fcis-22610	120	10	in	in	ADP
fcis-22610	120	11	the	the	DET
fcis-22610	120	12	final	final	ADJ
fcis-22610	120	13	output	output	NOUN
fcis-22610	120	14	accumulation	accumulation	NOUN
fcis-22610	120	15	stage	stage	NOUN
fcis-22610	120	16	,	,	PUNCT
fcis-22610	120	17	our	our	PRON
fcis-22610	120	18	restnetdownblockis	restnetdownblocki	NOUN
fcis-22610	120	19	increased	increase	VERB
fcis-22610	120	20	by	by	ADP
fcis-22610	120	21	1	1	NUM
fcis-22610	120	22	×	×	NOUN
fcis-22610	120	23	1	1	NUM
fcis-22610	120	24	.	.	PUNCT
fcis-22610	121	1	this	this	PRON
fcis-22610	121	2	can	can	AUX
fcis-22610	121	3	reduce	reduce	VERB
fcis-22610	121	4	the	the	DET
fcis-22610	121	5	number	number	NOUN
fcis-22610	121	6	of	of	ADP
fcis-22610	121	7	parameters	parameter	NOUN
fcis-22610	121	8	in	in	ADP
fcis-22610	121	9	the	the	DET
fcis-22610	121	10	deep	deep	ADJ
fcis-22610	121	11	network	network	NOUN
fcis-22610	121	12	and	and	CCONJ
fcis-22610	121	13	improve	improve	VERB
fcis-22610	121	14	efficiency	efficiency	NOUN
fcis-22610	121	15	.	.	PUNCT
fcis-22610	122	1	the	the	DET
fcis-22610	122	2	joint	joint	ADJ
fcis-22610	122	3	effect	effect	NOUN
fcis-22610	122	4	of	of	ADP
fcis-22610	122	5	the	the	DET
fcis-22610	122	6	two	two	NUM
fcis-22610	122	7	structures	structure	NOUN
fcis-22610	122	8	is	be	AUX
fcis-22610	122	9	to	to	PART
fcis-22610	122	10	deepen	deepen	VERB
fcis-22610	122	11	the	the	DET
fcis-22610	122	12	network	network	NOUN
fcis-22610	122	13	structure	structure	NOUN
fcis-22610	122	14	and	and	CCONJ
fcis-22610	122	15	improve	improve	VERB
fcis-22610	122	16	the	the	DET
fcis-22610	122	17	accuracy	accuracy	NOUN
fcis-22610	122	18	.	.	PUNCT
fcis-22610	123	1	at	at	ADP
fcis-22610	123	2	the	the	DET
fcis-22610	123	3	same	same	ADJ
fcis-22610	123	4	time	time	NOUN
fcis-22610	123	5	,	,	PUNCT
fcis-22610	123	6	it	it	PRON
fcis-22610	123	7	can	can	AUX
fcis-22610	123	8	solve	solve	VERB
fcis-22610	123	9	the	the	DET
fcis-22610	123	10	problem	problem	NOUN
fcis-22610	123	11	of	of	ADP
fcis-22610	123	12	network	network	NOUN
fcis-22610	123	13	degradation	degradation	NOUN
fcis-22610	123	14	,	,	PUNCT
fcis-22610	123	15	so	so	SCONJ
fcis-22610	123	16	as	as	SCONJ
fcis-22610	123	17	to	to	PART
fcis-22610	123	18	achieve	achieve	VERB
fcis-22610	123	19	a	a	DET
fcis-22610	123	20	more	more	ADV
fcis-22610	123	21	efficient	efficient	ADJ
fcis-22610	123	22	feature	feature	NOUN
fcis-22610	123	23	extraction	extraction	NOUN
fcis-22610	123	24	.	.	PUNCT
fcis-22610	124	1	64	64	NUM
fcis-22610	124	2	×64×	×64×	NOUN
fcis-22610	124	3	64	64	NUM
fcis-22610	124	4	images	image	NOUN
fcis-22610	124	5	are	be	AUX
fcis-22610	124	6	input	input	VERB
fcis-22610	124	7	into	into	ADP
fcis-22610	124	8	our	our	PRON
fcis-22610	124	9	encoder	encoder	NOUN
fcis-22610	124	10	,	,	PUNCT
fcis-22610	124	11	first	first	ADV
fcis-22610	124	12	through	through	ADP
fcis-22610	124	13	a	a	DET
fcis-22610	124	14	layer	layer	NOUN
fcis-22610	124	15	of	of	ADP
fcis-22610	124	16	convolution	convolution	NOUN
fcis-22610	124	17	kernel	kernel	NOUN
fcis-22610	124	18	with	with	ADP
fcis-22610	124	19	a	a	DET
fcis-22610	124	20	size	size	NOUN
fcis-22610	124	21	of	of	ADP
fcis-22610	124	22	7	7	NUM
fcis-22610	124	23	×	×	NOUN
fcis-22610	124	24	7	7	NUM
fcis-22610	124	25	.	.	PUNCT
fcis-22610	125	1	the	the	DET
fcis-22610	125	2	step	step	NOUN
fcis-22610	125	3	size	size	NOUN
fcis-22610	125	4	is	be	AUX
fcis-22610	125	5	1	1	NUM
fcis-22610	125	6	and	and	CCONJ
fcis-22610	125	7	the	the	DET
fcis-22610	125	8	paddingis	paddingis	ADJ
fcis-22610	125	9	3	3	NUM
fcis-22610	125	10	,	,	PUNCT
fcis-22610	125	11	which	which	PRON
fcis-22610	125	12	can	can	AUX
fcis-22610	125	13	better	well	ADV
fcis-22610	125	14	focus	focus	VERB
fcis-22610	125	15	the	the	DET
fcis-22610	125	16	required	require	VERB
fcis-22610	125	17	feature	feature	NOUN
fcis-22610	125	18	information	information	NOUN
fcis-22610	125	19	.	.	PUNCT
fcis-22610	126	1	then	then	ADV
fcis-22610	126	2	through	through	ADP
fcis-22610	126	3	maxpool	maxpool	NOUN
fcis-22610	126	4	and	and	CCONJ
fcis-22610	126	5	the	the	DET
fcis-22610	126	6	two	two	NUM
fcis-22610	126	7	residual	residual	ADJ
fcis-22610	126	8	structures	structure	NOUN
fcis-22610	126	9	we	we	PRON
fcis-22610	126	10	designed	design	VERB
fcis-22610	126	11	,	,	PUNCT
fcis-22610	126	12	and	and	CCONJ
fcis-22610	126	13	finally	finally	ADV
fcis-22610	126	14	through	through	ADP
fcis-22610	126	15	linearization	linearization	NOUN
fcis-22610	126	16	,	,	PUNCT
fcis-22610	126	17	the	the	DET
fcis-22610	126	18	output	output	NOUN
fcis-22610	126	19	data	data	NOUN
fcis-22610	126	20	is	be	AUX
fcis-22610	126	21	added	add	VERB
fcis-22610	126	22	to	to	ADP
fcis-22610	126	23	the	the	DET
fcis-22610	126	24	potential	potential	ADJ
fcis-22610	126	25	space	space	NOUN
fcis-22610	126	26	.	.	PUNCT
fcis-22610	127	1	the	the	DET
fcis-22610	127	2	encoder	encoder	NOUN
fcis-22610	127	3	we	we	PRON
fcis-22610	127	4	designed	design	VERB
fcis-22610	127	5	reduces	reduce	VERB
fcis-22610	127	6	the	the	DET
fcis-22610	127	7	number	number	NOUN
fcis-22610	127	8	of	of	ADP
fcis-22610	127	9	parameters	parameter	NOUN
fcis-22610	127	10	of	of	ADP
fcis-22610	127	11	resnet	resnet	ADJ
fcis-22610	127	12	network	network	NOUN
fcis-22610	127	13	to	to	ADP
fcis-22610	127	14	3.8	3.8	NUM
fcis-22610	127	15	million	million	NUM
fcis-22610	127	16	.	.	PUNCT
fcis-22610	128	1	the	the	DET
fcis-22610	128	2	number	number	NOUN
fcis-22610	128	3	of	of	ADP
fcis-22610	128	4	parameters	parameter	NOUN
fcis-22610	128	5	of	of	ADP
fcis-22610	128	6	the	the	DET
fcis-22610	128	7	original	original	ADJ
fcis-22610	128	8	resnet18	resnet18	NOUN
fcis-22610	128	9	network	network	NOUN
fcis-22610	128	10	far	far	ADV
fcis-22610	128	11	exceeds	exceed	VERB
fcis-22610	128	12	this	this	DET
fcis-22610	128	13	number	number	NOUN
fcis-22610	128	14	,	,	PUNCT
fcis-22610	128	15	reaching	reach	VERB
fcis-22610	128	16	the	the	DET
fcis-22610	128	17	level	level	NOUN
fcis-22610	128	18	oftens	often	VERB
fcis-22610	128	19	of	of	ADP
fcis-22610	128	20	millions	million	NOUN
fcis-22610	128	21	.	.	PUNCT
fcis-22610	129	1	leakyrelu	leakyrelu	NOUN
fcis-22610	130	1	[	[	X
fcis-22610	130	2	27	27	NUM
fcis-22610	130	3	]	]	PUNCT
fcis-22610	130	4	is	be	AUX
fcis-22610	130	5	added	add	VERB
fcis-22610	130	6	to	to	ADP
fcis-22610	130	7	the	the	DET
fcis-22610	130	8	neural	neural	ADJ
fcis-22610	130	9	network	network	NOUN
fcis-22610	130	10	to	to	PART
fcis-22610	130	11	introduce	introduce	VERB
fcis-22610	130	12	nonlinearity	nonlinearity	NOUN
fcis-22610	130	13	.	.	PUNCT
fcis-22610	131	1	at	at	ADP
fcis-22610	131	2	the	the	DET
fcis-22610	131	3	same	same	ADJ
fcis-22610	131	4	time	time	NOUN
fcis-22610	131	5	,	,	PUNCT
fcis-22610	131	6	it	it	PRON
fcis-22610	131	7	solves	solve	VERB
fcis-22610	131	8	the	the	DET
fcis-22610	131	9	problem	problem	NOUN
fcis-22610	131	10	of	of	ADP
fcis-22610	131	11	relu	relu	NOUN
fcis-22610	131	12	neuron	neuron	PROPN
fcis-22610	131	13	death	death	NOUN
fcis-22610	131	14	.	.	PUNCT
fcis-22610	132	1	it	it	PRON
fcis-22610	132	2	improves	improve	VERB
fcis-22610	132	3	the	the	DET
fcis-22610	132	4	efficiency	efficiency	NOUN
fcis-22610	132	5	and	and	CCONJ
fcis-22610	132	6	diversity	diversity	NOUN
fcis-22610	132	7	of	of	ADP
fcis-22610	132	8	feature	feature	NOUN
fcis-22610	132	9	extraction	extraction	NOUN
fcis-22610	132	10	.	.	PUNCT
fcis-22610	133	1	4	4	X
fcis-22610	133	2	.	.	X
fcis-22610	133	3	experiment	experiment	NOUN
fcis-22610	133	4	and	and	CCONJ
fcis-22610	133	5	evaluation	evaluation	NOUN
fcis-22610	133	6	in	in	ADP
fcis-22610	133	7	this	this	DET
fcis-22610	133	8	section	section	NOUN
fcis-22610	133	9	,	,	PUNCT
fcis-22610	133	10	we	we	PRON
fcis-22610	133	11	will	will	AUX
fcis-22610	133	12	conduct	conduct	VERB
fcis-22610	133	13	experimental	experimental	ADJ
fcis-22610	133	14	evaluation	evaluation	NOUN
fcis-22610	133	15	of	of	ADP
fcis-22610	133	16	our	our	PRON
fcis-22610	133	17	method	method	NOUN
fcis-22610	133	18	and	and	CCONJ
fcis-22610	133	19	simulate	simulate	VERB
fcis-22610	133	20	different	different	ADJ
fcis-22610	133	21	rope	rope	NOUN
fcis-22610	133	22	operation	operation	NOUN
fcis-22610	133	23	tasks	task	NOUN
fcis-22610	133	24	in	in	ADP
fcis-22610	133	25	different	different	ADJ
fcis-22610	133	26	environments	environment	NOUN
fcis-22610	133	27	.	.	PUNCT
fcis-22610	134	1	we	we	PRON
fcis-22610	134	2	will	will	AUX
fcis-22610	134	3	compare	compare	VERB
fcis-22610	134	4	different	different	ADJ
fcis-22610	134	5	models	model	NOUN
fcis-22610	134	6	on	on	ADP
fcis-22610	134	7	a	a	DET
fcis-22610	134	8	unified	unified	ADJ
fcis-22610	134	9	dataset	dataset	NOUN
fcis-22610	134	10	to	to	PART
fcis-22610	134	11	prove	prove	VERB
fcis-22610	134	12	that	that	SCONJ
fcis-22610	134	13	our	our	PRON
fcis-22610	134	14	method	method	NOUN
fcis-22610	134	15	really	really	ADV
fcis-22610	134	16	works	work	VERB
fcis-22610	134	17	.	.	PUNCT
fcis-22610	135	1	it	it	PRON
fcis-22610	135	2	also	also	ADV
fcis-22610	135	3	addresses	address	VERB
fcis-22610	135	4	the	the	DET
fcis-22610	135	5	question	question	NOUN
fcis-22610	135	6	of	of	ADP
fcis-22610	135	7	whether	whether	SCONJ
fcis-22610	135	8	contrastive	contrastive	ADJ
fcis-22610	135	9	learning	learning	NOUN
fcis-22610	135	10	for	for	ADP
fcis-22610	135	11	improved	improved	ADJ
fcis-22610	135	12	encoders	encoder	NOUN
fcis-22610	135	13	has	have	AUX
fcis-22610	135	14	a	a	DET
fcis-22610	135	15	better	well	ADJ
fcis-22610	135	16	potential	potential	NOUN
fcis-22610	135	17	for	for	ADP
fcis-22610	135	18	manipulation	manipulation	NOUN
fcis-22610	135	19	of	of	ADP
fcis-22610	135	20	deformable	deformable	ADJ
fcis-22610	135	21	linear	linear	ADJ
fcis-22610	135	22	objects	object	NOUN
fcis-22610	135	23	.	.	PUNCT
fcis-22610	136	1	later	later	ADV
fcis-22610	136	2	in	in	ADP
fcis-22610	136	3	this	this	DET
fcis-22610	136	4	section	section	NOUN
fcis-22610	136	5	,	,	PUNCT
fcis-22610	136	6	we	we	PRON
fcis-22610	136	7	will	will	AUX
fcis-22610	136	8	also	also	ADV
fcis-22610	136	9	conduct	conduct	VERB
fcis-22610	136	10	ablation	ablation	NOUN
fcis-22610	136	11	experiments	experiment	NOUN
fcis-22610	136	12	of	of	ADP
fcis-22610	136	13	separate	separate	ADJ
fcis-22610	136	14	modules	module	NOUN
fcis-22610	136	15	on	on	ADP
fcis-22610	136	16	the	the	DET
fcis-22610	136	17	same	same	ADJ
fcis-22610	136	18	dataset	dataset	NOUN
fcis-22610	136	19	,	,	PUNCT
fcis-22610	136	20	demonstrating	demonstrate	VERB
fcis-22610	136	21	the	the	DET
fcis-22610	136	22	layer	layer	NOUN
fcis-22610	136	23	-	-	PUNCT
fcis-22610	136	24	by	by	ADP
fcis-22610	136	25	-	-	PUNCT
fcis-22610	136	26	layer	layer	NOUN
fcis-22610	136	27	progress	progress	NOUN
fcis-22610	136	28	of	of	ADP
fcis-22610	136	29	our	our	PRON
fcis-22610	136	30	method	method	NOUN
fcis-22610	136	31	.	.	PUNCT
fcis-22610	137	1	4.1	4.1	NUM
fcis-22610	137	2	.	.	PUNCT
fcis-22610	137	3	data	datum	NOUN
fcis-22610	137	4	collection	collection	NOUN
fcis-22610	137	5	the	the	DET
fcis-22610	137	6	deep	deep	ADJ
fcis-22610	137	7	mind	mind	NOUN
fcis-22610	137	8	control	control	NOUN
fcis-22610	138	1	[	[	X
fcis-22610	138	2	33	33	NUM
fcis-22610	138	3	]	]	X
fcis-22610	138	4	platform	platform	NOUN
fcis-22610	138	5	of	of	ADP
fcis-22610	138	6	the	the	DET
fcis-22610	138	7	mujoco	mujoco	NOUN
fcis-22610	138	8	[	[	X
fcis-22610	138	9	34	34	NUM
fcis-22610	138	10	]	]	PUNCT
fcis-22610	138	11	engine	engine	NOUN
fcis-22610	138	12	we	we	PRON
fcis-22610	138	13	use	use	VERB
fcis-22610	138	14	uses	use	VERB
fcis-22610	138	15	a	a	DET
fcis-22610	138	16	64×64×3	64×64×3	NUM
fcis-22610	138	17	rgb	rgb	PROPN
fcis-22610	138	18	image	image	NOUN
fcis-22610	138	19	as	as	ADP
fcis-22610	138	20	input	input	NOUN
fcis-22610	138	21	.	.	PUNCT
fcis-22610	139	1	the	the	DET
fcis-22610	139	2	deformable	deformable	ADJ
fcis-22610	139	3	linear	linear	ADJ
fcis-22610	139	4	object	object	NOUN
fcis-22610	139	5	we	we	PRON
fcis-22610	139	6	designed	design	VERB
fcis-22610	139	7	is	be	AUX
fcis-22610	139	8	a	a	DET
fcis-22610	139	9	rope	rope	NOUN
fcis-22610	139	10	composed	compose	VERB
fcis-22610	139	11	of	of	ADP
fcis-22610	139	12	25	25	NUM
fcis-22610	139	13	geometric	geometric	ADJ
fcis-22610	139	14	bodies	body	NOUN
fcis-22610	139	15	,	,	PUNCT
fcis-22610	139	16	as	as	SCONJ
fcis-22610	139	17	shown	show	VERB
fcis-22610	139	18	in	in	ADP
fcis-22610	139	19	figure	figure	NOUN
fcis-22610	139	20	4	4	NUM
fcis-22610	139	21	.	.	PUNCT
fcis-22610	140	1	supervised	supervised	ADJ
fcis-22610	140	2	learning	learning	NOUN
fcis-22610	140	3	is	be	AUX
fcis-22610	140	4	expensive	expensive	ADJ
fcis-22610	140	5	and	and	CCONJ
fcis-22610	140	6	time	time	NOUN
fcis-22610	140	7	-	-	PUNCT
fcis-22610	140	8	consuming	consume	VERB
fcis-22610	140	9	to	to	PART
fcis-22610	140	10	collect	collect	VERB
fcis-22610	140	11	samples	sample	NOUN
fcis-22610	140	12	for	for	ADP
fcis-22610	140	13	manual	manual	ADJ
fcis-22610	140	14	labeling	labeling	NOUN
fcis-22610	140	15	,	,	PUNCT
fcis-22610	140	16	while	while	SCONJ
fcis-22610	140	17	comparative	comparative	ADJ
fcis-22610	140	18	learning	learning	NOUN
fcis-22610	140	19	like	like	ADP
fcis-22610	140	20	selfsupervised	selfsupervise	VERB
fcis-22610	140	21	learning	learning	NOUN
fcis-22610	140	22	does	do	AUX
fcis-22610	140	23	not	not	PART
fcis-22610	140	24	require	require	VERB
fcis-22610	140	25	manual	manual	ADJ
fcis-22610	140	26	labeling	labeling	NOUN
fcis-22610	140	27	information	information	NOUN
fcis-22610	140	28	,	,	PUNCT
fcis-22610	140	29	and	and	CCONJ
fcis-22610	140	30	directly	directly	ADV
fcis-22610	140	31	uses	use	VERB
fcis-22610	140	32	the	the	DET
fcis-22610	140	33	data	datum	NOUN
fcis-22610	140	34	itself	itself	PRON
fcis-22610	140	35	as	as	ADP
fcis-22610	140	36	supervision	supervision	NOUN
fcis-22610	140	37	information	information	NOUN
fcis-22610	140	38	to	to	PART
fcis-22610	140	39	learn	learn	VERB
fcis-22610	140	40	the	the	DET
fcis-22610	140	41	feature	feature	NOUN
fcis-22610	140	42	expression	expression	NOUN
fcis-22610	140	43	of	of	ADP
fcis-22610	140	44	sample	sample	NOUN
fcis-22610	140	45	data	datum	NOUN
fcis-22610	140	46	and	and	CCONJ
fcis-22610	140	47	use	use	VERB
fcis-22610	140	48	it	it	PRON
fcis-22610	140	49	as	as	ADP
fcis-22610	140	50	a	a	DET
fcis-22610	140	51	downstream	downstream	ADJ
fcis-22610	140	52	task	task	NOUN
fcis-22610	140	53	,	,	PUNCT
fcis-22610	140	54	then	then	ADV
fcis-22610	140	55	we	we	PRON
fcis-22610	140	56	solve	solve	VERB
fcis-22610	140	57	this	this	DET
fcis-22610	140	58	problem	problem	NOUN
fcis-22610	140	59	by	by	ADP
fcis-22610	140	60	perturbing	perturb	VERB
fcis-22610	140	61	the	the	DET
fcis-22610	140	62	rope	rope	NOUN
fcis-22610	140	63	through	through	ADP
fcis-22610	140	64	random	random	ADJ
fcis-22610	140	65	actions	action	NOUN
fcis-22610	140	66	in	in	ADP
fcis-22610	140	67	the	the	DET
fcis-22610	140	68	simulation	simulation	NOUN
fcis-22610	140	69	environment	environment	NOUN
fcis-22610	140	70	.	.	PUNCT
fcis-22610	141	1	we	we	PRON
fcis-22610	141	2	collected	collect	VERB
fcis-22610	141	3	20,000	20,000	NUM
fcis-22610	141	4	rope	rope	NOUN
fcis-22610	141	5	trajectories	trajectory	NOUN
fcis-22610	141	6	with	with	ADP
fcis-22610	141	7	a	a	DET
fcis-22610	141	8	length	length	NOUN
fcis-22610	141	9	of	of	ADP
fcis-22610	141	10	10	10	NUM
fcis-22610	141	11	(	(	PUNCT
fcis-22610	141	12	200k	200k	NUM
fcis-22610	141	13	samples	sample	NOUN
fcis-22610	141	14	)	)	PUNCT
fcis-22610	141	15	.	.	PUNCT
fcis-22610	142	1	part	part	NOUN
fcis-22610	142	2	of	of	ADP
fcis-22610	142	3	the	the	DET
fcis-22610	142	4	rope	rope	NOUN
fcis-22610	142	5	with	with	ADP
fcis-22610	142	6	different	different	ADJ
fcis-22610	142	7	trajectories	trajectory	NOUN
fcis-22610	142	8	.	.	PUNCT
fcis-22610	143	1	figure	figure	VERB
fcis-22610	143	2	4	4	NUM
fcis-22610	143	3	.	.	PUNCT
fcis-22610	144	1	the	the	DET
fcis-22610	144	2	first	first	ADJ
fcis-22610	144	3	two	two	NUM
fcis-22610	144	4	figures	figure	NOUN
fcis-22610	144	5	are	be	AUX
fcis-22610	144	6	generated	generate	VERB
fcis-22610	144	7	pictures	picture	NOUN
fcis-22610	144	8	,	,	PUNCT
fcis-22610	144	9	and	and	CCONJ
fcis-22610	144	10	the	the	DET
fcis-22610	144	11	last	last	ADJ
fcis-22610	144	12	two	two	NUM
fcis-22610	144	13	pictures	picture	NOUN
fcis-22610	144	14	are	be	AUX
fcis-22610	144	15	25	25	NUM
fcis-22610	144	16	geom	geom	PROPN
fcis-22610	144	17	simulation	simulation	PROPN
fcis-22610	144	18	environment	environment	NOUN
fcis-22610	144	19	ropes	rope	VERB
fcis-22610	144	20	4.2	4.2	NUM
fcis-22610	144	21	.	.	PUNCT
fcis-22610	145	1	baselines	baseline	NOUN
fcis-22610	145	2	training	train	VERB
fcis-22610	145	3	we	we	PRON
fcis-22610	145	4	employ	employ	VERB
fcis-22610	145	5	several	several	ADJ
fcis-22610	145	6	methods	method	NOUN
fcis-22610	145	7	to	to	PART
fcis-22610	145	8	compare	compare	VERB
fcis-22610	145	9	our	our	PRON
fcis-22610	145	10	improvements	improvement	NOUN
fcis-22610	145	11	,	,	PUNCT
fcis-22610	145	12	41	41	NUM
fcis-22610	145	13	a	a	DET
fcis-22610	145	14	visual	visual	ADJ
fcis-22610	145	15	forward	forward	ADV
fcis-22610	145	16	model[38	model[38	PROPN
fcis-22610	145	17	]	]	X
fcis-22610	145	18	,	,	PUNCT
fcis-22610	145	19	an	an	DET
fcis-22610	145	20	autoencoder	autoencoder	NOUN
fcis-22610	145	21	trained	train	VERB
fcis-22610	145	22	jointly	jointly	ADV
fcis-22610	145	23	with	with	ADP
fcis-22610	145	24	a	a	DET
fcis-22610	145	25	latent	latent	NOUN
fcis-22610	145	26	dynamics	dynamic	NOUN
fcis-22610	145	27	model[37	model[37	NOUN
fcis-22610	145	28	]	]	X
fcis-22610	145	29	,	,	PUNCT
fcis-22610	145	30	and	and	CCONJ
fcis-22610	145	31	planet	planet	NOUN
fcis-22610	145	32	[	[	X
fcis-22610	145	33	36	36	NUM
fcis-22610	145	34	]	]	PUNCT
fcis-22610	145	35	.	.	PUNCT
fcis-22610	146	1	and	and	CCONJ
fcis-22610	146	2	wilson	wilson	PROPN
fcis-22610	146	3	yai	yai	PROPN
fcis-22610	146	4	's	's	PART
fcis-22610	146	5	unimproved	unimproved	ADJ
fcis-22610	146	6	contrastive	contrastive	ADJ
fcis-22610	146	7	learning	learning	NOUN
fcis-22610	146	8	-	-	PUNCT
fcis-22610	146	9	based	base	VERB
fcis-22610	146	10	control	control	NOUN
fcis-22610	146	11	prediction	prediction	NOUN
fcis-22610	146	12	[	[	X
fcis-22610	146	13	12	12	NUM
fcis-22610	146	14	]	]	PUNCT
fcis-22610	146	15	.	.	PUNCT
fcis-22610	147	1	to	to	PART
fcis-22610	147	2	ensure	ensure	VERB
fcis-22610	147	3	that	that	SCONJ
fcis-22610	147	4	the	the	DET
fcis-22610	147	5	action	action	NOUN
fcis-22610	147	6	point	point	NOUN
fcis-22610	147	7	is	be	AUX
fcis-22610	147	8	on	on	ADP
fcis-22610	147	9	the	the	DET
fcis-22610	147	10	rope	rope	NOUN
fcis-22610	147	11	lock	lock	NOUN
fcis-22610	147	12	,	,	PUNCT
fcis-22610	147	13	we	we	PRON
fcis-22610	147	14	use	use	VERB
fcis-22610	147	15	rgb	rgb	PROPN
fcis-22610	147	16	thresholds	threshold	NOUN
fcis-22610	147	17	to	to	PART
fcis-22610	147	18	limit	limit	VERB
fcis-22610	147	19	its	its	PRON
fcis-22610	147	20	action	action	NOUN
fcis-22610	147	21	point	point	NOUN
fcis-22610	147	22	,	,	PUNCT
fcis-22610	147	23	and	and	CCONJ
fcis-22610	147	24	the	the	DET
fcis-22610	147	25	mpc	mpc	NOUN
fcis-22610	147	26	method	method	NOUN
fcis-22610	147	27	of	of	ADP
fcis-22610	147	28	onestep	onestep	NOUN
fcis-22610	147	29	prediction	prediction	NOUN
fcis-22610	147	30	is	be	AUX
fcis-22610	147	31	used	use	VERB
fcis-22610	147	32	in	in	ADP
fcis-22610	147	33	these	these	DET
fcis-22610	147	34	methods	method	NOUN
fcis-22610	147	35	.	.	PUNCT
fcis-22610	148	1	visual	visual	ADJ
fcis-22610	148	2	forward	forward	PROPN
fcis-22610	148	3	model	model	PROPN
fcis-22610	148	4	:	:	PUNCT
fcis-22610	148	5	kaise	kaise	PROPN
fcis-22610	148	6	's	's	PART
fcis-22610	148	7	forward	forward	ADJ
fcis-22610	148	8	model	model	NOUN
fcis-22610	148	9	is	be	AUX
fcis-22610	148	10	used	use	VERB
fcis-22610	148	11	to	to	PART
fcis-22610	148	12	model	model	VERB
fcis-22610	148	13	the	the	DET
fcis-22610	148	14	pixel	pixel	PROPN
fcis-22610	148	15	space	space	NOUN
fcis-22610	148	16	to	to	PART
fcis-22610	148	17	control	control	VERB
fcis-22610	148	18	the	the	DET
fcis-22610	148	19	prediction	prediction	NOUN
fcis-22610	148	20	.	.	PUNCT
fcis-22610	149	1	autoencoder	autoencoder	NOUN
fcis-22610	149	2	:	:	PUNCT
fcis-22610	149	3	compress	compress	VERB
fcis-22610	149	4	the	the	DET
fcis-22610	149	5	model	model	NOUN
fcis-22610	149	6	trained	train	VERB
fcis-22610	149	7	on	on	ADP
fcis-22610	149	8	the	the	DET
fcis-22610	149	9	latent	latent	NOUN
fcis-22610	149	10	space	space	NOUN
fcis-22610	149	11	via	via	ADP
fcis-22610	149	12	a	a	DET
fcis-22610	149	13	classical	classical	ADJ
fcis-22610	149	14	autoencoder	autoencoder	NOUN
fcis-22610	149	15	and	and	CCONJ
fcis-22610	149	16	a	a	DET
fcis-22610	149	17	simple	simple	ADJ
fcis-22610	149	18	forward	forward	ADJ
fcis-22610	149	19	dynamics	dynamic	NOUN
fcis-22610	149	20	model	model	NOUN
fcis-22610	149	21	.	.	PUNCT
fcis-22610	150	1	planet	planet	NOUN
fcis-22610	150	2	:	:	PUNCT
fcis-22610	150	3	by	by	ADP
fcis-22610	150	4	simulating	simulate	VERB
fcis-22610	150	5	a	a	DET
fcis-22610	150	6	vae	vae	PROPN
fcis-22610	150	7	variational	variational	PROPN
fcis-22610	150	8	autoencoder	autoencoder	NOUN
fcis-22610	150	9	,	,	PUNCT
fcis-22610	150	10	the	the	DET
fcis-22610	150	11	training	training	NOUN
fcis-22610	150	12	and	and	CCONJ
fcis-22610	150	13	learning	learning	NOUN
fcis-22610	150	14	of	of	ADP
fcis-22610	150	15	the	the	DET
fcis-22610	150	16	latent	latent	NOUN
fcis-22610	150	17	space	space	NOUN
fcis-22610	150	18	are	be	AUX
fcis-22610	150	19	performed	perform	VERB
fcis-22610	150	20	,	,	PUNCT
fcis-22610	150	21	and	and	CCONJ
fcis-22610	150	22	the	the	DET
fcis-22610	150	23	temporal	temporal	ADJ
fcis-22610	150	24	variation	variation	NOUN
fcis-22610	150	25	downline	downline	NOUN
fcis-22610	150	26	is	be	AUX
fcis-22610	150	27	optimized	optimize	VERB
fcis-22610	150	28	.	.	PUNCT
fcis-22610	151	1	cfm	cfm	NOUN
fcis-22610	151	2	:	:	PUNCT
fcis-22610	151	3	based	base	VERB
fcis-22610	151	4	on	on	ADP
fcis-22610	151	5	the	the	DET
fcis-22610	151	6	sample	sample	NOUN
fcis-22610	151	7	processing	processing	NOUN
fcis-22610	151	8	of	of	ADP
fcis-22610	151	9	contrastive	contrastive	ADJ
fcis-22610	151	10	learning	learning	NOUN
fcis-22610	151	11	,	,	PUNCT
fcis-22610	151	12	the	the	DET
fcis-22610	151	13	latent	latent	NOUN
fcis-22610	151	14	space	space	NOUN
fcis-22610	151	15	is	be	AUX
fcis-22610	151	16	trained	train	VERB
fcis-22610	151	17	and	and	CCONJ
fcis-22610	151	18	learned	learn	VERB
fcis-22610	151	19	through	through	ADP
fcis-22610	151	20	a	a	DET
fcis-22610	151	21	simple	simple	ADJ
fcis-22610	151	22	6	6	NUM
fcis-22610	151	23	-	-	PUNCT
fcis-22610	151	24	layer	layer	NOUN
fcis-22610	151	25	convolutional	convolutional	ADJ
fcis-22610	151	26	encoder	encoder	NOUN
fcis-22610	151	27	.	.	PUNCT
fcis-22610	152	1	we	we	PRON
fcis-22610	152	2	have	have	VERB
fcis-22610	152	3	the	the	DET
fcis-22610	152	4	same	same	ADJ
fcis-22610	152	5	environment	environment	NOUN
fcis-22610	152	6	in	in	ADP
fcis-22610	152	7	all	all	DET
fcis-22610	152	8	methods	method	NOUN
fcis-22610	152	9	,	,	PUNCT
fcis-22610	152	10	and	and	CCONJ
fcis-22610	152	11	all	all	DET
fcis-22610	152	12	ropes	rope	NOUN
fcis-22610	152	13	have	have	VERB
fcis-22610	152	14	the	the	DET
fcis-22610	152	15	same	same	ADJ
fcis-22610	152	16	number	number	NOUN
fcis-22610	152	17	and	and	CCONJ
fcis-22610	152	18	length	length	NOUN
fcis-22610	152	19	.	.	PUNCT
fcis-22610	153	1	we	we	PRON
fcis-22610	153	2	collected	collect	VERB
fcis-22610	153	3	the	the	DET
fcis-22610	153	4	predictions	prediction	NOUN
fcis-22610	153	5	of	of	ADP
fcis-22610	153	6	all	all	DET
fcis-22610	153	7	its	its	PRON
fcis-22610	153	8	methods	method	NOUN
fcis-22610	153	9	to	to	PART
fcis-22610	153	10	compare	compare	VERB
fcis-22610	153	11	with	with	ADP
fcis-22610	153	12	our	our	PRON
fcis-22610	153	13	method	method	NOUN
fcis-22610	153	14	and	and	CCONJ
fcis-22610	153	15	the	the	DET
fcis-22610	153	16	predicted	predict	VERB
fcis-22610	153	17	trajectory	trajectory	NOUN
fcis-22610	153	18	of	of	ADP
fcis-22610	153	19	each	each	DET
fcis-22610	153	20	method	method	NOUN
fcis-22610	153	21	.	.	PUNCT
fcis-22610	154	1	4.3	4.3	NUM
fcis-22610	154	2	.	.	PUNCT
fcis-22610	155	1	training	train	VERB
fcis-22610	155	2	the	the	DET
fcis-22610	155	3	encoder	encoder	NOUN
fcis-22610	155	4	we	we	PRON
fcis-22610	155	5	use	use	VERB
fcis-22610	155	6	is	be	AUX
fcis-22610	155	7	a	a	DET
fcis-22610	155	8	convolutional	convolutional	ADJ
fcis-22610	155	9	neural	neural	ADJ
fcis-22610	155	10	network	network	NOUN
fcis-22610	155	11	with	with	ADP
fcis-22610	155	12	a	a	DET
fcis-22610	155	13	residual	residual	ADJ
fcis-22610	155	14	structure	structure	NOUN
fcis-22610	155	15	.	.	PUNCT
fcis-22610	156	1	the	the	DET
fcis-22610	156	2	encoder	encoder	NOUN
fcis-22610	156	3	first	first	ADV
fcis-22610	156	4	goes	go	VERB
fcis-22610	156	5	through	through	ADP
fcis-22610	156	6	a	a	DET
fcis-22610	156	7	twodimensional	twodimensional	ADJ
fcis-22610	156	8	convolution	convolution	NOUN
fcis-22610	156	9	with	with	ADP
fcis-22610	156	10	a	a	DET
fcis-22610	156	11	kernel	kernel	NOUN
fcis-22610	156	12	size	size	NOUN
fcis-22610	156	13	of	of	ADP
fcis-22610	156	14	7	7	NUM
fcis-22610	156	15	and	and	CCONJ
fcis-22610	156	16	a	a	DET
fcis-22610	156	17	stride	stride	NOUN
fcis-22610	156	18	of	of	ADP
fcis-22610	156	19	1	1	NUM
fcis-22610	156	20	.	.	PUNCT
fcis-22610	157	1	after	after	ADP
fcis-22610	157	2	the	the	DET
fcis-22610	157	3	maximum	maximum	ADJ
fcis-22610	157	4	pooling	pool	VERB
fcis-22610	157	5	layer	layer	NOUN
fcis-22610	157	6	,	,	PUNCT
fcis-22610	157	7	the	the	DET
fcis-22610	157	8	amount	amount	NOUN
fcis-22610	157	9	of	of	ADP
fcis-22610	157	10	computation	computation	NOUN
fcis-22610	157	11	is	be	AUX
fcis-22610	157	12	reduced	reduce	VERB
fcis-22610	157	13	to	to	PART
fcis-22610	157	14	remove	remove	VERB
fcis-22610	157	15	redundant	redundant	ADJ
fcis-22610	157	16	structures	structure	NOUN
fcis-22610	157	17	and	and	CCONJ
fcis-22610	157	18	reduce	reduce	VERB
fcis-22610	157	19	our	our	PRON
fcis-22610	157	20	parameters	parameter	NOUN
fcis-22610	157	21	are	be	AUX
fcis-22610	157	22	then	then	ADV
fcis-22610	157	23	added	add	VERB
fcis-22610	157	24	to	to	ADP
fcis-22610	157	25	the	the	DET
fcis-22610	157	26	restnetbasicblock	restnetbasicblock	NOUN
fcis-22610	157	27	and	and	CCONJ
fcis-22610	157	28	restnetdownblock	restnetdownblock	NOUN
fcis-22610	157	29	layers	layer	NOUN
fcis-22610	157	30	,	,	PUNCT
fcis-22610	157	31	and	and	CCONJ
fcis-22610	157	32	leakyrelu	leakyrelu	NOUN
fcis-22610	158	1	[	[	X
fcis-22610	158	2	27	27	NUM
fcis-22610	158	3	]	]	PUNCT
fcis-22610	158	4	activations	activation	NOUN
fcis-22610	158	5	are	be	AUX
fcis-22610	158	6	added	add	VERB
fcis-22610	158	7	between	between	ADP
fcis-22610	158	8	each	each	DET
fcis-22610	158	9	layer	layer	NOUN
fcis-22610	158	10	,	,	PUNCT
fcis-22610	158	11	and	and	CCONJ
fcis-22610	158	12	the	the	DET
fcis-22610	158	13	final	final	ADJ
fcis-22610	158	14	output	output	NOUN
fcis-22610	158	15	is	be	AUX
fcis-22610	158	16	flattened	flatten	VERB
fcis-22610	158	17	and	and	CCONJ
fcis-22610	158	18	passed	pass	VERB
fcis-22610	158	19	into	into	ADP
fcis-22610	158	20	the	the	DET
fcis-22610	158	21	fully	fully	ADV
fcis-22610	158	22	linked	link	VERB
fcis-22610	158	23	layer	layer	NOUN
fcis-22610	158	24	.	.	PUNCT
fcis-22610	159	1	our	our	PRON
fcis-22610	159	2	kernel	kernel	PROPN
fcis-22610	159	3	size	size	NOUN
fcis-22610	159	4	[	[	X
fcis-22610	159	5	7,3,1,3,3,3,3,1,3,3	7,3,1,3,3,3,3,1,3,3	NOUN
fcis-22610	159	6	,	,	PUNCT
fcis-22610	159	7	3,3,1,3	3,3,1,3	NUM
fcis-22610	159	8	,	,	PUNCT
fcis-22610	159	9	3,3,3,1	3,3,3,1	NUM
fcis-22610	159	10	]	]	PUNCT
fcis-22610	159	11	per	per	ADP
fcis-22610	159	12	layer	layer	NOUN
fcis-22610	159	13	for	for	ADP
fcis-22610	159	14	our	our	PRON
fcis-22610	159	15	method	method	NOUN
fcis-22610	159	16	,	,	PUNCT
fcis-22610	159	17	we	we	PRON
fcis-22610	159	18	use	use	VERB
fcis-22610	159	19	contrastive	contrastive	ADJ
fcis-22610	159	20	learning	learning	NOUN
fcis-22610	159	21	with	with	ADP
fcis-22610	159	22	127	127	NUM
fcis-22610	159	23	negative	negative	ADJ
fcis-22610	159	24	samples	sample	NOUN
fcis-22610	159	25	per	per	ADP
fcis-22610	159	26	batch	batch	NOUN
fcis-22610	159	27	.	.	PUNCT
fcis-22610	160	1	the	the	DET
fcis-22610	160	2	learning	learning	NOUN
fcis-22610	160	3	rate	rate	NOUN
fcis-22610	160	4	used	use	VERB
fcis-22610	160	5	is	be	AUX
fcis-22610	160	6	1e-3	1e-3	NUM
fcis-22610	160	7	,	,	PUNCT
fcis-22610	160	8	the	the	DET
fcis-22610	160	9	weight_decay	weight_decay	NOUN
fcis-22610	160	10	is	be	AUX
fcis-22610	160	11	1e-6	1e-6	NUM
fcis-22610	160	12	,	,	PUNCT
fcis-22610	160	13	the	the	DET
fcis-22610	160	14	optimizer	optimizer	NOUN
fcis-22610	160	15	is	be	AUX
fcis-22610	160	16	adam	adam	PROPN
fcis-22610	160	17	[	[	X
fcis-22610	160	18	21	21	NUM
fcis-22610	160	19	]	]	PUNCT
fcis-22610	160	20	,	,	PUNCT
fcis-22610	160	21	and	and	CCONJ
fcis-22610	160	22	30	30	NUM
fcis-22610	160	23	epochs	epoch	NOUN
fcis-22610	160	24	are	be	AUX
fcis-22610	160	25	trained	train	VERB
fcis-22610	160	26	.	.	PUNCT
fcis-22610	161	1	all	all	DET
fcis-22610	161	2	methods	method	NOUN
fcis-22610	161	3	are	be	AUX
fcis-22610	161	4	trained	train	VERB
fcis-22610	161	5	on	on	ADP
fcis-22610	161	6	intel	intel	PROPN
fcis-22610	161	7	®	®	PROPN
fcis-22610	161	8	xeon(r	xeon(r	NOUN
fcis-22610	161	9	)	)	PUNCT
fcis-22610	161	10	w-2102	w-2102	PROPN
fcis-22610	161	11	cpu	cpu	VERB
fcis-22610	161	12	@	@	ADP
fcis-22610	161	13	2.90ghz	2.90ghz	NUM
fcis-22610	161	14	×	×	NOUN
fcis-22610	161	15	4	4	NUM
fcis-22610	161	16	and	and	CCONJ
fcis-22610	161	17	geforce	geforce	NOUN
fcis-22610	161	18	gtx	gtx	PROPN
fcis-22610	161	19	1050	1050	NUM
fcis-22610	161	20	ti	ti	NOUN
fcis-22610	161	21	.	.	PROPN
fcis-22610	161	22	4.4	4.4	NUM
fcis-22610	161	23	.	.	PUNCT
fcis-22610	162	1	quantitative	quantitative	ADJ
fcis-22610	162	2	assessment	assessment	NOUN
fcis-22610	162	3	in	in	ADP
fcis-22610	162	4	this	this	DET
fcis-22610	162	5	section	section	NOUN
fcis-22610	162	6	,	,	PUNCT
fcis-22610	162	7	we	we	PRON
fcis-22610	162	8	quantitatively	quantitatively	ADV
fcis-22610	162	9	evaluate	evaluate	VERB
fcis-22610	162	10	our	our	PRON
fcis-22610	162	11	method	method	NOUN
fcis-22610	162	12	against	against	ADP
fcis-22610	162	13	baseline	baseline	NOUN
fcis-22610	162	14	methods	method	NOUN
fcis-22610	162	15	.	.	PUNCT
fcis-22610	163	1	the	the	DET
fcis-22610	163	2	training	training	NOUN
fcis-22610	163	3	results	result	NOUN
fcis-22610	163	4	of	of	ADP
fcis-22610	163	5	all	all	DET
fcis-22610	163	6	our	our	PRON
fcis-22610	163	7	methods	method	NOUN
fcis-22610	163	8	are	be	AUX
fcis-22610	163	9	shown	show	VERB
fcis-22610	163	10	in	in	ADP
fcis-22610	163	11	table	table	NOUN
fcis-22610	163	12	1	1	NUM
fcis-22610	163	13	.	.	PUNCT
fcis-22610	164	1	the	the	DET
fcis-22610	164	2	evaluation	evaluation	NOUN
fcis-22610	164	3	unit	unit	NOUN
fcis-22610	164	4	used	use	VERB
fcis-22610	164	5	for	for	ADP
fcis-22610	164	6	the	the	DET
fcis-22610	164	7	evaluation	evaluation	NOUN
fcis-22610	164	8	of	of	ADP
fcis-22610	164	9	all	all	DET
fcis-22610	164	10	our	our	PRON
fcis-22610	164	11	methods	method	NOUN
fcis-22610	164	12	is	be	AUX
fcis-22610	164	13	geom	geom	NOUN
fcis-22610	164	14	,	,	PUNCT
fcis-22610	164	15	which	which	PRON
fcis-22610	164	16	represents	represent	VERB
fcis-22610	164	17	the	the	DET
fcis-22610	164	18	sum	sum	NOUN
fcis-22610	164	19	of	of	ADP
fcis-22610	164	20	the	the	DET
fcis-22610	164	21	geometric	geometric	ADJ
fcis-22610	164	22	distance	distance	NOUN
fcis-22610	164	23	between	between	ADP
fcis-22610	164	24	the	the	DET
fcis-22610	164	25	final	final	ADJ
fcis-22610	164	26	result	result	NOUN
fcis-22610	164	27	and	and	CCONJ
fcis-22610	164	28	the	the	DET
fcis-22610	164	29	target	target	NOUN
fcis-22610	164	30	result	result	VERB
fcis-22610	164	31	.	.	PUNCT
fcis-22610	165	1	it	it	PRON
fcis-22610	165	2	can	can	AUX
fcis-22610	165	3	be	be	AUX
fcis-22610	165	4	found	find	VERB
fcis-22610	165	5	that	that	SCONJ
fcis-22610	165	6	our	our	PRON
fcis-22610	165	7	proposed	propose	VERB
fcis-22610	165	8	method	method	NOUN
fcis-22610	165	9	has	have	VERB
fcis-22610	165	10	obvious	obvious	ADJ
fcis-22610	165	11	advantages	advantage	NOUN
fcis-22610	165	12	in	in	ADP
fcis-22610	165	13	different	different	ADJ
fcis-22610	165	14	angles	angle	NOUN
fcis-22610	165	15	,	,	PUNCT
fcis-22610	165	16	and	and	CCONJ
fcis-22610	165	17	the	the	DET
fcis-22610	165	18	error	error	NOUN
fcis-22610	165	19	is	be	AUX
fcis-22610	165	20	the	the	DET
fcis-22610	165	21	smallest	small	ADJ
fcis-22610	165	22	in	in	ADP
fcis-22610	165	23	horizontal	horizontal	ADJ
fcis-22610	165	24	angles	angle	NOUN
fcis-22610	165	25	.	.	PUNCT
fcis-22610	166	1	this	this	PRON
fcis-22610	166	2	shows	show	VERB
fcis-22610	166	3	that	that	SCONJ
fcis-22610	166	4	our	our	PRON
fcis-22610	166	5	method	method	NOUN
fcis-22610	166	6	has	have	VERB
fcis-22610	166	7	better	well	ADJ
fcis-22610	166	8	generalization	generalization	NOUN
fcis-22610	166	9	ability	ability	NOUN
fcis-22610	166	10	and	and	CCONJ
fcis-22610	166	11	better	well	ADJ
fcis-22610	166	12	performance	performance	NOUN
fcis-22610	166	13	in	in	ADP
fcis-22610	166	14	the	the	DET
fcis-22610	166	15	latent	latent	NOUN
fcis-22610	166	16	space	space	NOUN
fcis-22610	166	17	,	,	PUNCT
fcis-22610	166	18	which	which	PRON
fcis-22610	166	19	proves	prove	VERB
fcis-22610	166	20	that	that	SCONJ
fcis-22610	166	21	our	our	PRON
fcis-22610	166	22	method	method	NOUN
fcis-22610	166	23	is	be	AUX
fcis-22610	166	24	indeed	indeed	ADV
fcis-22610	166	25	effective	effective	ADJ
fcis-22610	166	26	and	and	CCONJ
fcis-22610	166	27	has	have	AUX
fcis-22610	166	28	progressed	progress	VERB
fcis-22610	166	29	.	.	PUNCT
fcis-22610	167	1	all	all	DET
fcis-22610	167	2	methods	method	NOUN
fcis-22610	167	3	run	run	VERB
fcis-22610	167	4	20	20	NUM
fcis-22610	167	5	actions	action	NOUN
fcis-22610	167	6	in	in	ADP
fcis-22610	167	7	the	the	DET
fcis-22610	167	8	same	same	ADJ
fcis-22610	167	9	environment	environment	NOUN
fcis-22610	167	10	with	with	ADP
fcis-22610	167	11	the	the	DET
fcis-22610	167	12	same	same	ADJ
fcis-22610	167	13	initial	initial	ADJ
fcis-22610	167	14	state	state	NOUN
fcis-22610	167	15	and	and	CCONJ
fcis-22610	167	16	target	target	VERB
fcis-22610	167	17	state	state	NOUN
fcis-22610	167	18	,	,	PUNCT
fcis-22610	167	19	and	and	CCONJ
fcis-22610	167	20	our	our	PRON
fcis-22610	167	21	method	method	NOUN
fcis-22610	167	22	first	first	ADV
fcis-22610	167	23	reaches	reach	VERB
fcis-22610	167	24	the	the	DET
fcis-22610	167	25	target	target	NOUN
fcis-22610	167	26	state	state	NOUN
fcis-22610	167	27	.	.	PUNCT
fcis-22610	168	1	as	as	SCONJ
fcis-22610	168	2	shown	show	VERB
fcis-22610	168	3	figure	figure	NOUN
fcis-22610	168	4	5	5	NUM
fcis-22610	168	5	.	.	NOUN
fcis-22610	168	6	4.5	4.5	NUM
fcis-22610	168	7	.	.	PUNCT
fcis-22610	168	8	ablation	ablation	NOUN
fcis-22610	168	9	experiment	experiment	NOUN
fcis-22610	168	10	in	in	ADP
fcis-22610	168	11	this	this	DET
fcis-22610	168	12	section	section	NOUN
fcis-22610	168	13	,	,	PUNCT
fcis-22610	168	14	we	we	PRON
fcis-22610	168	15	will	will	AUX
fcis-22610	168	16	perform	perform	VERB
fcis-22610	168	17	ablation	ablation	NOUN
fcis-22610	168	18	experiments	experiment	NOUN
fcis-22610	168	19	on	on	ADP
fcis-22610	168	20	network	network	NOUN
fcis-22610	168	21	structures	structure	NOUN
fcis-22610	168	22	and	and	CCONJ
fcis-22610	168	23	datasets	dataset	NOUN
fcis-22610	168	24	to	to	PART
fcis-22610	168	25	demonstrate	demonstrate	VERB
fcis-22610	168	26	the	the	DET
fcis-22610	168	27	superiority	superiority	NOUN
fcis-22610	168	28	of	of	ADP
fcis-22610	168	29	our	our	PRON
fcis-22610	168	30	method	method	NOUN
fcis-22610	168	31	.	.	PUNCT
fcis-22610	169	1	(	(	PUNCT
fcis-22610	169	2	1	1	X
fcis-22610	169	3	)	)	PUNCT
fcis-22610	169	4	in	in	ADP
fcis-22610	169	5	the	the	DET
fcis-22610	169	6	first	first	ADJ
fcis-22610	169	7	experiment	experiment	NOUN
fcis-22610	169	8	,	,	PUNCT
fcis-22610	169	9	we	we	PRON
fcis-22610	169	10	will	will	AUX
fcis-22610	169	11	replace	replace	VERB
fcis-22610	169	12	the	the	DET
fcis-22610	169	13	simple	simple	ADJ
fcis-22610	169	14	convolutional	convolutional	ADJ
fcis-22610	169	15	network	network	NOUN
fcis-22610	169	16	in	in	ADP
fcis-22610	169	17	the	the	DET
fcis-22610	169	18	encoder	encoder	NOUN
fcis-22610	169	19	with	with	ADP
fcis-22610	169	20	a	a	DET
fcis-22610	169	21	new	new	ADJ
fcis-22610	169	22	network	network	NOUN
fcis-22610	169	23	with	with	ADP
fcis-22610	169	24	our	our	PRON
fcis-22610	169	25	improved	improve	VERB
fcis-22610	169	26	residual	residual	ADJ
fcis-22610	169	27	network	network	NOUN
fcis-22610	169	28	structure	structure	NOUN
fcis-22610	169	29	.	.	PUNCT
fcis-22610	170	1	the	the	DET
fcis-22610	170	2	original	original	ADJ
fcis-22610	170	3	simple	simple	ADJ
fcis-22610	170	4	convolutional	convolutional	ADJ
fcis-22610	170	5	network	network	NOUN
fcis-22610	170	6	is	be	AUX
fcis-22610	170	7	a	a	DET
fcis-22610	170	8	network	network	NOUN
fcis-22610	170	9	with	with	ADP
fcis-22610	170	10	only	only	ADV
fcis-22610	170	11	six	six	NUM
fcis-22610	170	12	layers	layer	NOUN
fcis-22610	170	13	of	of	ADP
fcis-22610	170	14	two	two	NUM
fcis-22610	170	15	-	-	PUNCT
fcis-22610	170	16	dimensional	dimensional	ADJ
fcis-22610	170	17	convolution	convolution	NOUN
fcis-22610	170	18	and	and	CCONJ
fcis-22610	170	19	relu	relu	NOUN
fcis-22610	170	20	activation	activation	NOUN
fcis-22610	170	21	functions	function	NOUN
fcis-22610	170	22	.	.	PUNCT
fcis-22610	171	1	the	the	DET
fcis-22610	171	2	complexity	complexity	NOUN
fcis-22610	171	3	of	of	ADP
fcis-22610	171	4	the	the	DET
fcis-22610	171	5	simple	simple	ADJ
fcis-22610	171	6	network	network	NOUN
fcis-22610	171	7	is	be	AUX
fcis-22610	171	8	low	low	ADJ
fcis-22610	171	9	,	,	PUNCT
fcis-22610	171	10	and	and	CCONJ
fcis-22610	171	11	the	the	DET
fcis-22610	171	12	extracted	extract	VERB
fcis-22610	171	13	information	information	NOUN
fcis-22610	171	14	is	be	AUX
fcis-22610	171	15	not	not	PART
fcis-22610	171	16	perfect	perfect	ADJ
fcis-22610	171	17	.	.	PUNCT
fcis-22610	172	1	and	and	CCONJ
fcis-22610	172	2	our	our	PRON
fcis-22610	172	3	network	network	NOUN
fcis-22610	172	4	has	have	VERB
fcis-22610	172	5	more	more	ADJ
fcis-22610	172	6	advantages	advantage	NOUN
fcis-22610	172	7	in	in	ADP
fcis-22610	172	8	feature	feature	NOUN
fcis-22610	172	9	information	information	NOUN
fcis-22610	172	10	extraction	extraction	NOUN
fcis-22610	172	11	.	.	PUNCT
fcis-22610	173	1	the	the	DET
fcis-22610	173	2	training	training	NOUN
fcis-22610	173	3	results	result	NOUN
fcis-22610	173	4	are	be	AUX
fcis-22610	173	5	shown	show	VERB
fcis-22610	173	6	in	in	ADP
fcis-22610	173	7	table	table	NOUN
fcis-22610	173	8	3	3	NUM
fcis-22610	173	9	.	.	PUNCT
fcis-22610	174	1	(	(	PUNCT
fcis-22610	174	2	2	2	X
fcis-22610	174	3	)	)	PUNCT
fcis-22610	174	4	in	in	ADP
fcis-22610	174	5	the	the	DET
fcis-22610	174	6	second	second	ADJ
fcis-22610	174	7	experiment	experiment	NOUN
fcis-22610	174	8	,	,	PUNCT
fcis-22610	174	9	we	we	PRON
fcis-22610	174	10	will	will	AUX
fcis-22610	174	11	discuss	discuss	VERB
fcis-22610	174	12	the	the	DET
fcis-22610	174	13	dataset	dataset	NOUN
fcis-22610	174	14	,	,	PUNCT
fcis-22610	174	15	adding	add	VERB
fcis-22610	174	16	domain	domain	NOUN
fcis-22610	174	17	randomization	randomization	NOUN
fcis-22610	174	18	,	,	PUNCT
fcis-22610	174	19	the	the	DET
fcis-22610	174	20	rest	rest	NOUN
fcis-22610	174	21	being	be	AUX
fcis-22610	174	22	the	the	DET
fcis-22610	174	23	same	same	ADJ
fcis-22610	174	24	.	.	PUNCT
fcis-22610	175	1	after	after	ADP
fcis-22610	175	2	adding	add	VERB
fcis-22610	175	3	this	this	DET
fcis-22610	175	4	operation	operation	NOUN
fcis-22610	175	5	,	,	PUNCT
fcis-22610	175	6	the	the	DET
fcis-22610	175	7	data	datum	NOUN
fcis-22610	175	8	set	set	VERB
fcis-22610	175	9	samples	sample	NOUN
fcis-22610	175	10	have	have	VERB
fcis-22610	175	11	a	a	DET
fcis-22610	175	12	variety	variety	NOUN
fcis-22610	175	13	of	of	ADP
fcis-22610	175	14	changes	change	NOUN
fcis-22610	175	15	,	,	PUNCT
fcis-22610	175	16	which	which	PRON
fcis-22610	175	17	will	will	AUX
fcis-22610	175	18	be	be	AUX
fcis-22610	175	19	more	more	ADV
fcis-22610	175	20	suitable	suitable	ADJ
fcis-22610	175	21	for	for	ADP
fcis-22610	175	22	the	the	DET
fcis-22610	175	23	display	display	NOUN
fcis-22610	175	24	environment	environment	NOUN
fcis-22610	175	25	,	,	PUNCT
fcis-22610	175	26	and	and	CCONJ
fcis-22610	175	27	it	it	PRON
fcis-22610	175	28	can	can	AUX
fcis-22610	175	29	be	be	AUX
fcis-22610	175	30	seen	see	VERB
fcis-22610	175	31	in	in	ADP
fcis-22610	175	32	the	the	DET
fcis-22610	175	33	simulation	simulation	NOUN
fcis-22610	175	34	test	test	NOUN
fcis-22610	175	35	that	that	SCONJ
fcis-22610	175	36	there	there	PRON
fcis-22610	175	37	is	be	VERB
fcis-22610	175	38	indeed	indeed	ADV
fcis-22610	175	39	progress	progress	NOUN
fcis-22610	175	40	.	.	PUNCT
fcis-22610	176	1	the	the	DET
fcis-22610	176	2	experimental	experimental	ADJ
fcis-22610	176	3	results	result	NOUN
fcis-22610	176	4	are	be	AUX
fcis-22610	176	5	shown	show	VERB
fcis-22610	176	6	in	in	ADP
fcis-22610	176	7	table	table	NOUN
fcis-22610	176	8	2	2	NUM
fcis-22610	176	9	.	.	PUNCT
fcis-22610	176	10	table	table	NOUN
fcis-22610	176	11	1	1	NUM
fcis-22610	176	12	.	.	PUNCT
fcis-22610	177	1	quantitative	quantitative	ADJ
fcis-22610	177	2	comparison	comparison	NOUN
fcis-22610	177	3	between	between	ADP
fcis-22610	177	4	different	different	ADJ
fcis-22610	177	5	methods	method	NOUN
fcis-22610	177	6	of	of	ADP
fcis-22610	177	7	manipulation	manipulation	NOUN
fcis-22610	177	8	in	in	ADP
fcis-22610	177	9	the	the	DET
fcis-22610	177	10	rope	rope	NOUN
fcis-22610	177	11	task	task	NOUN
fcis-22610	177	12	.	.	PUNCT
fcis-22610	178	1	the	the	DET
fcis-22610	178	2	evaluation	evaluation	NOUN
fcis-22610	178	3	unit	unit	NOUN
fcis-22610	178	4	is	be	AUX
fcis-22610	178	5	the	the	DET
fcis-22610	178	6	geometric	geometric	ADJ
fcis-22610	178	7	distance	distance	NOUN
fcis-22610	178	8	(	(	PUNCT
fcis-22610	178	9	geom	geom	NOUN
fcis-22610	178	10	)	)	PUNCT
fcis-22610	178	11	between	between	ADP
fcis-22610	178	12	the	the	DET
fcis-22610	178	13	final	final	ADJ
fcis-22610	178	14	task	task	NOUN
fcis-22610	178	15	and	and	CCONJ
fcis-22610	178	16	the	the	DET
fcis-22610	178	17	target	target	NOUN
fcis-22610	178	18	state	state	NOUN
fcis-22610	178	19	.	.	PUNCT
fcis-22610	179	1	the	the	DET
fcis-22610	179	2	smaller	small	ADJ
fcis-22610	179	3	the	the	DET
fcis-22610	179	4	distance	distance	NOUN
fcis-22610	179	5	,	,	PUNCT
fcis-22610	179	6	the	the	DET
fcis-22610	179	7	better	well	ADJ
fcis-22610	179	8	.	.	PUNCT
fcis-22610	180	1	rope	rope	NOUN
fcis-22610	180	2	method	method	NOUN
fcis-22610	180	3	90	90	NUM
fcis-22610	180	4	。	。	NOUN
fcis-22610	180	5	0	0	NUM
fcis-22610	180	6	。	。	SYM
fcis-22610	180	7	135	135	NUM
fcis-22610	180	8	。	。	NUM
fcis-22610	180	9	45	45	NUM
fcis-22610	180	10	。	。	NUM
fcis-22610	180	11	random	random	ADJ
fcis-22610	180	12	visual	visual	ADJ
fcis-22610	180	13	forward	forward	ADJ
fcis-22610	180	14	model	model	NOUN
fcis-22610	180	15	2.38	2.38	NUM
fcis-22610	180	16	2.09	2.09	NUM
fcis-22610	180	17	2.10	2.10	NUM
fcis-22610	180	18	2.29	2.29	NUM
fcis-22610	180	19	1.52	1.52	NUM
fcis-22610	180	20	autoencoder	autoencoder	NOUN
fcis-22610	180	21	1.96	1.96	NUM
fcis-22610	180	22	1.72	1.72	NUM
fcis-22610	180	23	1.85	1.85	NUM
fcis-22610	180	24	2.11	2.11	NUM
fcis-22610	180	25	4.30	4.30	NUM
fcis-22610	180	26	planet	planet	NOUN
fcis-22610	180	27	2.58	2.58	NUM
fcis-22610	180	28	1.81	1.81	NUM
fcis-22610	180	29	2.18	2.18	NUM
fcis-22610	180	30	2.31	2.31	NUM
fcis-22610	180	31	3.03	3.03	NUM
fcis-22610	180	32	cfm	cfm	NOUN
fcis-22610	180	33	1.89	1.89	NUM
fcis-22610	180	34	0.58	0.58	NUM
fcis-22610	180	35	1.34	1.34	NUM
fcis-22610	180	36	2.29	2.29	NUM
fcis-22610	180	37	1.52	1.52	NUM
fcis-22610	180	38	our	our	PRON
fcis-22610	180	39	1.07	1.07	NUM
fcis-22610	180	40	0.48	0.48	NUM
fcis-22610	180	41	0.62	0.62	NUM
fcis-22610	180	42	1.01	1.01	NUM
fcis-22610	180	43	1.40	1.40	NUM
fcis-22610	180	44	table	table	NOUN
fcis-22610	180	45	2	2	NUM
fcis-22610	180	46	.	.	PUNCT
fcis-22610	181	1	the	the	DET
fcis-22610	181	2	data	datum	NOUN
fcis-22610	181	3	set	set	NOUN
fcis-22610	181	4	has	have	AUX
fcis-22610	181	5	undergone	undergo	VERB
fcis-22610	181	6	domain	domain	NOUN
fcis-22610	181	7	randomization	randomization	NOUN
fcis-22610	181	8	to	to	PART
fcis-22610	181	9	adapt	adapt	VERB
fcis-22610	181	10	it	it	PRON
fcis-22610	181	11	to	to	ADP
fcis-22610	181	12	more	more	ADJ
fcis-22610	181	13	environments	environment	NOUN
fcis-22610	181	14	.	.	PUNCT
fcis-22610	182	1	in	in	ADP
fcis-22610	182	2	quantity	quantity	NOUN
fcis-22610	182	3	evaluation	evaluation	NOUN
fcis-22610	182	4	of	of	ADP
fcis-22610	182	5	various	various	ADJ
fcis-22610	182	6	methods	method	NOUN
fcis-22610	182	7	on	on	ADP
fcis-22610	182	8	its	its	PRON
fcis-22610	182	9	data	data	NOUN
fcis-22610	182	10	set	set	VERB
fcis-22610	182	11	,	,	PUNCT
fcis-22610	182	12	the	the	DET
fcis-22610	182	13	evaluation	evaluation	NOUN
fcis-22610	182	14	criteria	criterion	NOUN
fcis-22610	182	15	are	be	AUX
fcis-22610	182	16	the	the	DET
fcis-22610	182	17	same	same	ADJ
fcis-22610	182	18	as	as	ADP
fcis-22610	182	19	those	those	PRON
fcis-22610	182	20	in	in	ADP
fcis-22610	182	21	table	table	NOUN
fcis-22610	182	22	1	1	NUM
fcis-22610	182	23	.	.	PUNCT
fcis-22610	183	1	rope	rope	NOUN
fcis-22610	183	2	with	with	ADP
fcis-22610	183	3	dr	dr	PROPN
fcis-22610	183	4	method	method	PROPN
fcis-22610	183	5	90	90	NUM
fcis-22610	183	6	。	。	NUM
fcis-22610	183	7	0	0	NUM
fcis-22610	183	8	。	。	SYM
fcis-22610	183	9	135	135	NUM
fcis-22610	183	10	。	。	NUM
fcis-22610	183	11	45	45	NUM
fcis-22610	183	12	。	。	NUM
fcis-22610	183	13	random	random	ADJ
fcis-22610	183	14	visual	visual	ADJ
fcis-22610	183	15	forward	forward	NOUN
fcis-22610	183	16	model	model	NOUN
fcis-22610	183	17	5.28	5.28	NUM
fcis-22610	183	18	2.60	2.60	NUM
fcis-22610	183	19	4.07	4.07	NUM
fcis-22610	183	20	4.47	4.47	NUM
fcis-22610	183	21	4.59	4.59	NUM
fcis-22610	183	22	autoencoder	autoencoder	NOUN
fcis-22610	183	23	2.23	2.23	NUM
fcis-22610	183	24	1.79	1.79	NUM
fcis-22610	183	25	2.16	2.16	NUM
fcis-22610	183	26	2.05	2.05	NUM
fcis-22610	183	27	3.16	3.16	NUM
fcis-22610	183	28	planet	planet	NOUN
fcis-22610	183	29	2.22	2.22	NUM
fcis-22610	183	30	1.79	1.79	NUM
fcis-22610	183	31	2.12	2.12	NUM
fcis-22610	183	32	2.07	2.07	NUM
fcis-22610	183	33	2.80	2.80	NUM
fcis-22610	183	34	cfm	cfm	NOUN
fcis-22610	183	35	1.13	1.13	NUM
fcis-22610	183	36	0.68	0.68	NUM
fcis-22610	183	37	0.82	0.82	NUM
fcis-22610	183	38	0.92	0.92	NUM
fcis-22610	183	39	1.38	1.38	NUM
fcis-22610	183	40	our	our	PRON
fcis-22610	183	41	0.83	0.83	NUM
fcis-22610	183	42	0.47	0.47	NUM
fcis-22610	183	43	0.51	0.51	NUM
fcis-22610	183	44	0.73	0.73	NUM
fcis-22610	183	45	1.37	1.37	NUM
fcis-22610	183	46	42	42	NUM
fcis-22610	183	47	figure	figure	NOUN
fcis-22610	183	48	5	5	NUM
fcis-22610	183	49	.	.	PUNCT
fcis-22610	184	1	all	all	DET
fcis-22610	184	2	methods	method	NOUN
fcis-22610	184	3	run	run	VERB
fcis-22610	184	4	20	20	NUM
fcis-22610	184	5	actions	action	NOUN
fcis-22610	184	6	whose	whose	DET
fcis-22610	184	7	target	target	NOUN
fcis-22610	184	8	state	state	NOUN
fcis-22610	184	9	is	be	AUX
fcis-22610	184	10	the	the	DET
fcis-22610	184	11	rope	rope	NOUN
fcis-22610	184	12	vertical	vertical	NOUN
fcis-22610	184	13	,	,	PUNCT
fcis-22610	184	14	and	and	CCONJ
fcis-22610	184	15	we	we	PRON
fcis-22610	184	16	sample	sample	VERB
fcis-22610	184	17	every	every	DET
fcis-22610	184	18	4	4	NUM
fcis-22610	184	19	actions	action	NOUN
fcis-22610	184	20	.	.	PUNCT
fcis-22610	185	1	obviously	obviously	ADV
fcis-22610	185	2	our	our	PRON
fcis-22610	185	3	method	method	NOUN
fcis-22610	185	4	reaches	reach	VERB
fcis-22610	185	5	the	the	DET
fcis-22610	185	6	target	target	NOUN
fcis-22610	185	7	state	state	NOUN
fcis-22610	185	8	first	first	ADJ
fcis-22610	185	9	table	table	NOUN
fcis-22610	185	10	3	3	NUM
fcis-22610	185	11	.	.	PUNCT
fcis-22610	186	1	regarding	regard	VERB
fcis-22610	186	2	the	the	DET
fcis-22610	186	3	ablation	ablation	NOUN
fcis-22610	186	4	experiments	experiment	NOUN
fcis-22610	186	5	of	of	ADP
fcis-22610	186	6	the	the	DET
fcis-22610	186	7	encoder	encoder	NOUN
fcis-22610	186	8	,	,	PUNCT
fcis-22610	186	9	all	all	DET
fcis-22610	186	10	the	the	DET
fcis-22610	186	11	criteria	criterion	NOUN
fcis-22610	186	12	are	be	AUX
fcis-22610	186	13	the	the	DET
fcis-22610	186	14	same	same	ADJ
fcis-22610	186	15	,	,	PUNCT
fcis-22610	186	16	only	only	ADV
fcis-22610	186	17	the	the	DET
fcis-22610	186	18	network	network	NOUN
fcis-22610	186	19	model	model	NOUN
fcis-22610	186	20	of	of	ADP
fcis-22610	186	21	the	the	DET
fcis-22610	186	22	encoder	encoder	NOUN
fcis-22610	186	23	is	be	AUX
fcis-22610	186	24	different	different	ADJ
fcis-22610	186	25	,	,	PUNCT
fcis-22610	186	26	and	and	CCONJ
fcis-22610	186	27	the	the	DET
fcis-22610	186	28	evaluation	evaluation	NOUN
fcis-22610	186	29	criteria	criterion	NOUN
fcis-22610	186	30	are	be	AUX
fcis-22610	186	31	the	the	DET
fcis-22610	186	32	same	same	ADJ
fcis-22610	186	33	as	as	ADP
fcis-22610	186	34	table	table	NOUN
fcis-22610	186	35	1	1	NUM
fcis-22610	186	36	.	.	PUNCT
fcis-22610	186	37	method	method	PROPN
fcis-22610	186	38	90	90	NUM
fcis-22610	186	39	。	。	NOUN
fcis-22610	186	40	0	0	NUM
fcis-22610	186	41	。	。	SYM
fcis-22610	186	42	135	135	NUM
fcis-22610	186	43	。	。	NUM
fcis-22610	186	44	45	45	NUM
fcis-22610	186	45	。	。	NUM
fcis-22610	186	46	random	random	ADJ
fcis-22610	186	47	simple	simple	ADJ
fcis-22610	186	48	1.8	1.8	NUM
fcis-22610	186	49	05	05	NUM
fcis-22610	186	50	1.3	1.3	NUM
fcis-22610	186	51	2.2	2.2	NUM
fcis-22610	186	52	1.5	1.5	NUM
fcis-22610	186	53	ours	ours	PRON
fcis-22610	186	54	1.0	1.0	NUM
fcis-22610	186	55	0.4	0.4	NUM
fcis-22610	186	56	0.6	0.6	NUM
fcis-22610	186	57	1.0	1.0	NUM
fcis-22610	186	58	1.4	1.4	NUM
fcis-22610	186	59	5	5	NUM
fcis-22610	186	60	.	.	PUNCT
fcis-22610	186	61	conclusion	conclusion	NOUN
fcis-22610	186	62	in	in	ADP
fcis-22610	186	63	this	this	DET
fcis-22610	186	64	paper	paper	NOUN
fcis-22610	186	65	,	,	PUNCT
fcis-22610	186	66	we	we	PRON
fcis-22610	186	67	focus	focus	VERB
fcis-22610	186	68	on	on	ADP
fcis-22610	186	69	modifying	modify	VERB
fcis-22610	186	70	the	the	DET
fcis-22610	186	71	encoder	encoder	NOUN
fcis-22610	186	72	network	network	NOUN
fcis-22610	186	73	structure	structure	NOUN
fcis-22610	186	74	,	,	PUNCT
fcis-22610	186	75	deepening	deepen	VERB
fcis-22610	186	76	the	the	DET
fcis-22610	186	77	network	network	NOUN
fcis-22610	186	78	structure	structure	NOUN
fcis-22610	186	79	and	and	CCONJ
fcis-22610	186	80	avoiding	avoid	VERB
fcis-22610	186	81	network	network	NOUN
fcis-22610	186	82	degradation	degradation	NOUN
fcis-22610	186	83	and	and	CCONJ
fcis-22610	186	84	over	over	ADP
fcis-22610	186	85	fitting	fitting	ADJ
fcis-22610	186	86	.	.	PUNCT
fcis-22610	187	1	under	under	ADP
fcis-22610	187	2	the	the	DET
fcis-22610	187	3	condition	condition	NOUN
fcis-22610	187	4	of	of	ADP
fcis-22610	187	5	appropriate	appropriate	ADJ
fcis-22610	187	6	parameters	parameter	NOUN
fcis-22610	187	7	,	,	PUNCT
fcis-22610	187	8	it	it	PRON
fcis-22610	187	9	greatly	greatly	ADV
fcis-22610	187	10	improves	improve	VERB
fcis-22610	187	11	the	the	DET
fcis-22610	187	12	extraction	extraction	NOUN
fcis-22610	187	13	ability	ability	NOUN
fcis-22610	187	14	of	of	ADP
fcis-22610	187	15	feature	feature	NOUN
fcis-22610	187	16	information	information	NOUN
fcis-22610	187	17	,	,	PUNCT
fcis-22610	187	18	obtains	obtain	VERB
fcis-22610	187	19	more	more	ADV
fcis-22610	187	20	effective	effective	ADJ
fcis-22610	187	21	feature	feature	NOUN
fcis-22610	187	22	information	information	NOUN
fcis-22610	187	23	,	,	PUNCT
fcis-22610	187	24	and	and	CCONJ
fcis-22610	187	25	makes	make	VERB
fcis-22610	187	26	the	the	DET
fcis-22610	187	27	final	final	ADJ
fcis-22610	187	28	state	state	NOUN
fcis-22610	187	29	of	of	ADP
fcis-22610	187	30	rope	rope	NOUN
fcis-22610	187	31	shape	shape	NOUN
fcis-22610	187	32	closer	close	ADV
fcis-22610	187	33	to	to	ADP
fcis-22610	187	34	the	the	DET
fcis-22610	187	35	target	target	NOUN
fcis-22610	187	36	state	state	NOUN
fcis-22610	187	37	.	.	PUNCT
fcis-22610	188	1	in	in	ADP
fcis-22610	188	2	the	the	DET
fcis-22610	188	3	future	future	ADJ
fcis-22610	188	4	work	work	NOUN
fcis-22610	188	5	,	,	PUNCT
fcis-22610	188	6	we	we	PRON
fcis-22610	188	7	will	will	AUX
fcis-22610	188	8	do	do	VERB
fcis-22610	188	9	more	more	ADJ
fcis-22610	188	10	experiments	experiment	NOUN
fcis-22610	188	11	on	on	ADP
fcis-22610	188	12	the	the	DET
fcis-22610	188	13	expansion	expansion	NOUN
fcis-22610	188	14	of	of	ADP
fcis-22610	188	15	deformable	deformable	ADJ
fcis-22610	188	16	objects	object	NOUN
fcis-22610	188	17	,	,	PUNCT
fcis-22610	188	18	from	from	ADP
fcis-22610	188	19	ropes	rope	NOUN
fcis-22610	188	20	to	to	ADP
fcis-22610	188	21	more	more	ADV
fcis-22610	188	22	deformable	deformable	ADJ
fcis-22610	188	23	flexible	flexible	ADJ
fcis-22610	188	24	objects	object	NOUN
fcis-22610	188	25	,	,	PUNCT
fcis-22610	188	26	so	so	SCONJ
fcis-22610	188	27	that	that	SCONJ
fcis-22610	188	28	they	they	PRON
fcis-22610	188	29	can	can	AUX
fcis-22610	188	30	achieve	achieve	VERB
fcis-22610	188	31	more	more	ADJ
fcis-22610	188	32	types	type	NOUN
fcis-22610	188	33	of	of	ADP
fcis-22610	188	34	operation	operation	NOUN
fcis-22610	188	35	tasks	task	NOUN
fcis-22610	188	36	.	.	PUNCT
fcis-22610	189	1	references	reference	NOUN
fcis-22610	189	2	[	[	X
fcis-22610	189	3	1	1	X
fcis-22610	189	4	]	]	X
fcis-22610	189	5	takahiro	takahiro	PROPN
fcis-22610	189	6	wada	wada	PROPN
fcis-22610	189	7	,	,	PUNCT
fcis-22610	189	8	shinichi	shinichi	PROPN
fcis-22610	189	9	hirai	hirai	PROPN
fcis-22610	189	10	,	,	PUNCT
fcis-22610	189	11	sadao	sadao	PROPN
fcis-22610	189	12	kawamura	kawamura	PROPN
fcis-22610	189	13	,	,	PUNCT
fcis-22610	189	14	and	and	CCONJ
fcis-22610	189	15	norimasa	norimasa	PROPN
fcis-22610	189	16	kamiji	kamiji	PROPN
fcis-22610	189	17	.	.	PUNCT
fcis-22610	190	1	robust	robust	ADJ
fcis-22610	190	2	manipulation	manipulation	NOUN
fcis-22610	190	3	of	of	ADP
fcis-22610	190	4	deformable	deformable	ADJ
fcis-22610	190	5	objects	object	NOUN
fcis-22610	190	6	by	by	ADP
fcis-22610	190	7	simple	simple	ADJ
fcis-22610	190	8	positive	positive	ADJ
fcis-22610	190	9	feedback	feedback	NOUN
fcis-22610	190	10	.	.	PUNCT
fcis-22610	191	1	in	in	ADP
fcis-22610	191	2	icra	icra	PROPN
fcis-22610	191	3	,	,	PUNCT
fcis-22610	191	4	2001	2001	NUM
fcis-22610	191	5	.	.	PUNCT
fcis-22610	192	1	[	[	X
fcis-22610	192	2	2	2	X
fcis-22610	192	3	]	]	PUNCT
fcis-22610	192	4	dominik	dominik	X
fcis-22610	192	5	henrich	henrich	PROPN
fcis-22610	192	6	and	and	CCONJ
fcis-22610	192	7	heinz	heinz	ADJ
fcis-22610	192	8	wörn	wörn	NOUN
fcis-22610	192	9	.	.	PUNCT
fcis-22610	193	1	robot	robot	NOUN
fcis-22610	193	2	manipulation	manipulation	NOUN
fcis-22610	193	3	of	of	ADP
fcis-22610	193	4	deformable	deformable	ADJ
fcis-22610	193	5	objects	object	NOUN
fcis-22610	193	6	.	.	PUNCT
fcis-22610	194	1	in	in	ADP
fcis-22610	194	2	springer	springer	NOUN
fcis-22610	194	3	science	science	PROPN
fcis-22610	194	4	&	&	CCONJ
fcis-22610	194	5	business	business	NOUN
fcis-22610	194	6	media	medium	NOUN
fcis-22610	194	7	,	,	PUNCT
fcis-22610	194	8	2012	2012	NUM
fcis-22610	194	9	.	.	PUNCT
fcis-22610	195	1	[	[	X
fcis-22610	195	2	3	3	X
fcis-22610	195	3	]	]	X
fcis-22610	195	4	john	john	PROPN
fcis-22610	195	5	schulman	schulman	PROPN
fcis-22610	195	6	,	,	PUNCT
fcis-22610	195	7	jonathan	jonathan	PROPN
fcis-22610	195	8	ho	ho	PROPN
fcis-22610	195	9	,	,	PUNCT
fcis-22610	195	10	cameron	cameron	PROPN
fcis-22610	195	11	lee	lee	PROPN
fcis-22610	195	12	,	,	PUNCT
fcis-22610	195	13	and	and	CCONJ
fcis-22610	195	14	pieter	pieter	NOUN
fcis-22610	195	15	abbeel	abbeel	NOUN
fcis-22610	195	16	.	.	PUNCT
fcis-22610	196	1	generalization	generalization	NOUN
fcis-22610	196	2	in	in	ADP
fcis-22610	196	3	robotic	robotic	ADJ
fcis-22610	196	4	manipulation	manipulation	NOUN
fcis-22610	196	5	through	through	ADP
fcis-22610	196	6	the	the	DET
fcis-22610	196	7	use	use	NOUN
fcis-22610	196	8	of	of	ADP
fcis-22610	196	9	nonrigid	nonrigid	ADJ
fcis-22610	196	10	registration	registration	NOUN
fcis-22610	196	11	.	.	PUNCT
fcis-22610	197	1	in	in	ADP
fcis-22610	197	2	isrr	isrr	NOUN
fcis-22610	197	3	,	,	PUNCT
fcis-22610	197	4	2013	2013	NUM
fcis-22610	197	5	.	.	PUNCT
fcis-22610	198	1	[	[	X
fcis-22610	198	2	4	4	X
fcis-22610	198	3	]	]	X
fcis-22610	198	4	john	john	PROPN
fcis-22610	198	5	schulman	schulman	PROPN
fcis-22610	198	6	,	,	PUNCT
fcis-22610	198	7	alex	alex	PROPN
fcis-22610	198	8	lee	lee	PROPN
fcis-22610	198	9	,	,	PUNCT
fcis-22610	198	10	jonathan	jonathan	PROPN
fcis-22610	198	11	ho	ho	PROPN
fcis-22610	198	12	,	,	PUNCT
fcis-22610	198	13	and	and	CCONJ
fcis-22610	198	14	pieter	pieter	NOUN
fcis-22610	198	15	abbeel	abbeel	NOUN
fcis-22610	198	16	.	.	PUNCT
fcis-22610	199	1	tracking	track	VERB
fcis-22610	199	2	deformable	deformable	ADJ
fcis-22610	199	3	objects	object	NOUN
fcis-22610	199	4	with	with	ADP
fcis-22610	199	5	point	point	NOUN
fcis-22610	199	6	clouds	cloud	NOUN
fcis-22610	199	7	.	.	PUNCT
fcis-22610	200	1	in	in	ADP
fcis-22610	200	2	icra	icra	PROPN
fcis-22610	200	3	,	,	PUNCT
fcis-22610	200	4	2013	2013	NUM
fcis-22610	200	5	.	.	PUNCT
fcis-22610	201	1	[	[	X
fcis-22610	201	2	5	5	X
fcis-22610	201	3	]	]	PUNCT
fcis-22610	201	4	yilin	yilin	PROPN
fcis-22610	201	5	wu	wu	PROPN
fcis-22610	201	6	,	,	PUNCT
fcis-22610	201	7	wilson	wilson	PROPN
fcis-22610	201	8	yan	yan	PROPN
fcis-22610	201	9	,	,	PUNCT
fcis-22610	201	10	thanard	thanard	PROPN
fcis-22610	201	11	kurutach	kurutach	PROPN
fcis-22610	201	12	,	,	PUNCT
fcis-22610	201	13	lerrel	lerrel	NOUN
fcis-22610	201	14	pinto	pinto	NOUN
fcis-22610	201	15	,	,	PUNCT
fcis-22610	201	16	and	and	CCONJ
fcis-22610	201	17	pieter	pieter	NOUN
fcis-22610	201	18	abbeel	abbeel	PROPN
fcis-22610	201	19	learning	learn	VERB
fcis-22610	201	20	to	to	PART
fcis-22610	201	21	manipulate	manipulate	VERB
fcis-22610	201	22	deformable	deformable	ADJ
fcis-22610	201	23	objects	object	NOUN
fcis-22610	201	24	without	without	ADP
fcis-22610	201	25	demonstrations	demonstration	NOUN
fcis-22610	201	26	.	.	PUNCT
fcis-22610	202	1	in	in	ADP
fcis-22610	202	2	arxiv	arxiv	PROPN
fcis-22610	202	3	preprint	preprint	NOUN
fcis-22610	202	4	,	,	PUNCT
fcis-22610	202	5	2019	2019	NUM
fcis-22610	202	6	.	.	PUNCT
fcis-22610	203	1	[	[	X
fcis-22610	203	2	6	6	NUM
fcis-22610	203	3	]	]	X
fcis-22610	203	4	daniel	daniel	PROPN
fcis-22610	203	5	seita	seita	PROPN
fcis-22610	203	6	,	,	PUNCT
fcis-22610	203	7	aditya	aditya	PROPN
fcis-22610	203	8	ganapathi	ganapathi	PROPN
fcis-22610	203	9	,	,	PUNCT
fcis-22610	203	10	ryan	ryan	PROPN
fcis-22610	203	11	hoque	hoque	NOUN
fcis-22610	203	12	,	,	PUNCT
fcis-22610	203	13	minho	minho	PROPN
fcis-22610	203	14	hwang	hwang	PROPN
fcis-22610	203	15	,	,	PUNCT
fcis-22610	203	16	edward	edward	PROPN
fcis-22610	203	17	cen	cen	PROPN
fcis-22610	203	18	,	,	PUNCT
fcis-22610	203	19	ajay	ajay	PROPN
fcis-22610	203	20	kumar	kumar	PROPN
fcis-22610	203	21	tanwani	tanwani	PROPN
fcis-22610	203	22	,	,	PUNCT
fcis-22610	203	23	ashwin	ashwin	PROPN
fcis-22610	203	24	balakrishna	balakrishna	PROPN
fcis-22610	203	25	,	,	PUNCT
fcis-22610	203	26	brijen	brijen	NOUN
fcis-22610	203	27	thananjeyan	thananjeyan	PROPN
fcis-22610	203	28	,	,	PUNCT
fcis-22610	203	29	jeffrey	jeffrey	PROPN
fcis-22610	203	30	ichnowski	ichnowski	PROPN
fcis-22610	203	31	,	,	PUNCT
fcis-22610	203	32	nawid	nawid	PROPN
fcis-22610	203	33	jamali	jamali	PROPN
fcis-22610	203	34	,	,	PUNCT
fcis-22610	203	35	katsu	katsu	PROPN
fcis-22610	203	36	yamane	yamane	PROPN
fcis-22610	203	37	,	,	PUNCT
fcis-22610	203	38	soshi	soshi	PROPN
fcis-22610	203	39	iba	iba	PROPN
fcis-22610	203	40	,	,	PUNCT
fcis-22610	203	41	john	john	PROPN
fcis-22610	203	42	canny	canny	PROPN
fcis-22610	203	43	,	,	PUNCT
fcis-22610	203	44	and	and	CCONJ
fcis-22610	203	45	ken	ken	PROPN
fcis-22610	203	46	goldberg	goldberg	PROPN
fcis-22610	203	47	.	.	PUNCT
fcis-22610	204	1	deep	deep	ADJ
fcis-22610	204	2	imitation	imitation	NOUN
fcis-22610	204	3	learning	learning	NOUN
fcis-22610	204	4	of	of	ADP
fcis-22610	204	5	sequential	sequential	ADJ
fcis-22610	204	6	fabric	fabric	NOUN
fcis-22610	204	7	smoothing	smoothing	NOUN
fcis-22610	204	8	policies	policy	NOUN
fcis-22610	204	9	.	.	PUNCT
fcis-22610	205	1	in	in	ADP
fcis-22610	205	2	arxiv	arxiv	PROPN
fcis-22610	205	3	preprint,2019	preprint,2019	VERB
fcis-22610	205	4	.	.	PUNCT
fcis-22610	206	1	[	[	X
fcis-22610	206	2	7	7	X
fcis-22610	206	3	]	]	X
fcis-22610	206	4	jeremy	jeremy	PROPN
fcis-22610	206	5	martin	martin	PROPN
fcis-22610	206	6	-	-	PUNCT
fcis-22610	206	7	shepard	shepard	NOUN
fcis-22610	206	8	,	,	PUNCT
fcis-22610	206	9	marco	marco	PROPN
fcis-22610	206	10	cusumano	cusumano	PROPN
fcis-22610	206	11	-	-	PUNCT
fcis-22610	206	12	towner	towner	NOUN
fcis-22610	206	13	,	,	PUNCT
fcis-22610	206	14	jinna	jinna	NOUN
fcis-22610	206	15	lei	lei	PROPN
fcis-22610	206	16	,	,	PUNCT
fcis-22610	206	17	and	and	CCONJ
fcis-22610	206	18	pieter	pieter	PROPN
fcis-22610	206	19	abbeel	abbeel	NOUN
fcis-22610	206	20	.	.	PUNCT
fcis-22610	207	1	cloth	cloth	NOUN
fcis-22610	207	2	grasp	grasp	NOUN
fcis-22610	207	3	point	point	NOUN
fcis-22610	207	4	detection	detection	NOUN
fcis-22610	207	5	based	base	VERB
fcis-22610	207	6	on	on	ADP
fcis-22610	207	7	multiple	multiple	ADJ
fcis-22610	207	8	-	-	PUNCT
fcis-22610	207	9	view	view	NOUN
fcis-22610	207	10	geometric	geometric	ADJ
fcis-22610	207	11	cues	cue	NOUN
fcis-22610	207	12	with	with	ADP
fcis-22610	207	13	application	application	NOUN
fcis-22610	207	14	to	to	ADP
fcis-22610	207	15	robotic	robotic	ADJ
fcis-22610	207	16	towel	towel	NOUN
fcis-22610	207	17	folding	folding	NOUN
fcis-22610	207	18	.	.	PUNCT
fcis-22610	208	1	in	in	ADP
fcis-22610	208	2	icra	icra	PROPN
fcis-22610	208	3	,	,	PUNCT
fcis-22610	208	4	2010	2010	NUM
fcis-22610	208	5	.	.	PUNCT
fcis-22610	209	1	[	[	X
fcis-22610	209	2	8	8	NUM
fcis-22610	209	3	]	]	PUNCT
fcis-22610	209	4	jan	jan	PROPN
fcis-22610	209	5	stria	stria	PROPN
fcis-22610	209	6	,	,	PUNCT
fcis-22610	209	7	daniel	daniel	PROPN
fcis-22610	209	8	prusa	prusa	PROPN
fcis-22610	209	9	,	,	PUNCT
fcis-22610	209	10	v	v	ADP
fcis-22610	209	11	aclav	aclav	NOUN
fcis-22610	209	12	hlavac	hlavac	PROPN
fcis-22610	209	13	,	,	PUNCT
fcis-22610	209	14	libor	libor	PROPN
fcis-22610	209	15	wagner	wagner	PROPN
fcis-22610	209	16	,	,	PUNCT
fcis-22610	209	17	vladimir	vladimir	PROPN
fcis-22610	209	18	petrik	petrik	PROPN
fcis-22610	209	19	,	,	PUNCT
fcis-22610	209	20	pavel	pavel	PROPN
fcis-22610	209	21	krsek	krsek	PROPN
fcis-22610	209	22	,	,	PUNCT
fcis-22610	209	23	and	and	CCONJ
fcis-22610	209	24	vladimir	vladimir	PROPN
fcis-22610	209	25	smutny	smutny	PROPN
fcis-22610	209	26	.	.	PUNCT
fcis-22610	210	1	garment	garment	NOUN
fcis-22610	210	2	perception	perception	NOUN
fcis-22610	210	3	and	and	CCONJ
fcis-22610	210	4	its	its	PRON
fcis-22610	210	5	folding	folding	NOUN
fcis-22610	210	6	using	use	VERB
fcis-22610	210	7	a	a	DET
fcis-22610	210	8	dual	dual	ADJ
fcis-22610	210	9	-	-	PUNCT
fcis-22610	210	10	arm	arm	NOUN
fcis-22610	210	11	robot	robot	NOUN
fcis-22610	210	12	.	.	PUNCT
fcis-22610	211	1	in	in	ADP
fcis-22610	211	2	iros	iro	NOUN
fcis-22610	211	3	,	,	PUNCT
fcis-22610	211	4	2014	2014	NUM
fcis-22610	211	5	.	.	PUNCT
fcis-22610	212	1	[	[	X
fcis-22610	212	2	9	9	NUM
fcis-22610	212	3	]	]	PUNCT
fcis-22610	212	4	tuomas	tuomas	PROPN
fcis-22610	212	5	haarnoja	haarnoja	PROPN
fcis-22610	212	6	,	,	PUNCT
fcis-22610	212	7	aurick	aurick	PROPN
fcis-22610	212	8	zhou	zhou	PROPN
fcis-22610	212	9	,	,	PUNCT
fcis-22610	212	10	kristian	kristian	PROPN
fcis-22610	212	11	hartikainen	hartikainen	PROPN
fcis-22610	212	12	,	,	PUNCT
fcis-22610	212	13	george	george	PROPN
fcis-22610	212	14	tucker	tucker	PROPN
fcis-22610	212	15	,	,	PUNCT
fcis-22610	212	16	sehoon	sehoon	NOUN
fcis-22610	212	17	ha	ha	INTJ
fcis-22610	212	18	,	,	PUNCT
fcis-22610	212	19	jie	jie	PROPN
fcis-22610	212	20	tan	tan	PROPN
fcis-22610	212	21	,	,	PUNCT
fcis-22610	212	22	vikash	vikash	PROPN
fcis-22610	212	23	kumar	kumar	PROPN
fcis-22610	212	24	,	,	PUNCT
fcis-22610	212	25	henry	henry	PROPN
fcis-22610	212	26	zhu	zhu	PROPN
fcis-22610	212	27	,	,	PUNCT
fcis-22610	212	28	abhishek	abhishek	PROPN
fcis-22610	212	29	gupta	gupta	PROPN
fcis-22610	212	30	,	,	PUNCT
fcis-22610	212	31	pieter	pieter	NOUN
fcis-22610	212	32	abbeel	abbeel	NOUN
fcis-22610	212	33	.	.	PUNCT
fcis-22610	213	1	soft	soft	ADJ
fcis-22610	213	2	actor	actor	NOUN
fcis-22610	213	3	-	-	PUNCT
fcis-22610	213	4	critic	critic	NOUN
fcis-22610	213	5	algorithms	algorithm	NOUN
fcis-22610	213	6	,	,	PUNCT
fcis-22610	213	7	and	and	CCONJ
fcis-22610	213	8	applications	application	NOUN
fcis-22610	213	9	.	.	PUNCT
fcis-22610	214	1	in	in	ADP
fcis-22610	214	2	arxiv	arxiv	PROPN
fcis-22610	214	3	preprint	preprint	NOUN
fcis-22610	214	4	,	,	PUNCT
fcis-22610	214	5	2018	2018	NUM
fcis-22610	214	6	.	.	PUNCT
fcis-22610	215	1	[	[	X
fcis-22610	215	2	10	10	NUM
fcis-22610	215	3	]	]	X
fcis-22610	215	4	john	john	PROPN
fcis-22610	215	5	schulman	schulman	PROPN
fcis-22610	215	6	,	,	PUNCT
fcis-22610	215	7	sergey	sergey	PROPN
fcis-22610	215	8	levine	levine	PROPN
fcis-22610	215	9	,	,	PUNCT
fcis-22610	215	10	pieter	pieter	PROPN
fcis-22610	215	11	abbeel	abbeel	PROPN
fcis-22610	215	12	,	,	PUNCT
fcis-22610	215	13	michael	michael	PROPN
fcis-22610	215	14	jordan	jordan	PROPN
fcis-22610	215	15	,	,	PUNCT
fcis-22610	215	16	and	and	CCONJ
fcis-22610	215	17	philipp	philipp	PROPN
fcis-22610	215	18	moritz	moritz	PROPN
fcis-22610	215	19	.	.	PUNCT
fcis-22610	215	20	trust	trust	PROPN
fcis-22610	215	21	region	region	NOUN
fcis-22610	215	22	policy	policy	NOUN
fcis-22610	215	23	optimization	optimization	NOUN
fcis-22610	215	24	.	.	PUNCT
fcis-22610	216	1	in	in	ADP
fcis-22610	216	2	icml,2015	icml,2015	PROPN
fcis-22610	216	3	.	.	PUNCT
fcis-22610	217	1	[	[	X
fcis-22610	217	2	11	11	NUM
fcis-22610	217	3	]	]	X
fcis-22610	217	4	timothy	timothy	PROPN
fcis-22610	217	5	plillicrap	plillicrap	PROPN
fcis-22610	217	6	,	,	PUNCT
fcis-22610	217	7	jonathan	jonathan	PROPN
fcis-22610	217	8	j	j	PROPN
fcis-22610	217	9	hunt	hunt	PROPN
fcis-22610	217	10	,	,	PUNCT
fcis-22610	217	11	alexander	alexander	PROPN
fcis-22610	217	12	pritzel	pritzel	PROPN
fcis-22610	217	13	,	,	PUNCT
fcis-22610	217	14	nicolas	nicolas	PROPN
fcis-22610	217	15	heess	heess	PROPN
fcis-22610	217	16	,	,	PUNCT
fcis-22610	217	17	tom	tom	PROPN
fcis-22610	217	18	erez	erez	PROPN
fcis-22610	217	19	,	,	PUNCT
fcis-22610	217	20	yuval	yuval	PROPN
fcis-22610	217	21	tassa	tassa	PROPN
fcis-22610	217	22	,	,	PUNCT
fcis-22610	217	23	david	david	PROPN
fcis-22610	217	24	silver	silver	PROPN
fcis-22610	217	25	,	,	PUNCT
fcis-22610	217	26	and	and	CCONJ
fcis-22610	217	27	daan	daan	PROPN
fcis-22610	217	28	wierstra	wierstra	PROPN
fcis-22610	217	29	.	.	PUNCT
fcis-22610	218	1	continuous	continuous	ADJ
fcis-22610	218	2	control	control	NOUN
fcis-22610	218	3	with	with	ADP
fcis-22610	218	4	deep	deep	ADJ
fcis-22610	218	5	reinforcement	reinforcement	NOUN
fcis-22610	218	6	learning	learning	NOUN
fcis-22610	218	7	.	.	PUNCT
fcis-22610	219	1	in	in	ADP
fcis-22610	219	2	arxiv	arxiv	PROPN
fcis-22610	219	3	preprint	preprint	NOUN
fcis-22610	219	4	,	,	PUNCT
fcis-22610	219	5	2015	2015	NUM
fcis-22610	219	6	.	.	PUNCT
fcis-22610	220	1	[	[	X
fcis-22610	220	2	12	12	NUM
fcis-22610	220	3	]	]	X
fcis-22610	220	4	wilson	wilson	PROPN
fcis-22610	220	5	yan	yan	PROPN
fcis-22610	220	6	,	,	PUNCT
fcis-22610	220	7	ashwin	ashwin	PROPN
fcis-22610	220	8	v	v	PROPN
fcis-22610	220	9	angipuram	angipuram	PROPN
fcis-22610	220	10	,	,	PUNCT
fcis-22610	220	11	pieter	pieter	NOUN
fcis-22610	220	12	abbeel	abbeel	NOUN
fcis-22610	220	13	,	,	PUNCT
fcis-22610	220	14	and	and	CCONJ
fcis-22610	220	15	lerrel	lerrel	NOUN
fcis-22610	220	16	pinto	pinto	NOUN
fcis-22610	220	17	,	,	PUNCT
fcis-22610	220	18	learning	learn	VERB
fcis-22610	220	19	predictive	predictive	ADJ
fcis-22610	220	20	representations	representation	NOUN
fcis-22610	220	21	for	for	ADP
fcis-22610	220	22	deformable	deformable	ADJ
fcis-22610	220	23	objects	object	NOUN
fcis-22610	220	24	using	use	VERB
fcis-22610	220	25	contrastive	contrastive	ADJ
fcis-22610	220	26	estimation	estimation	NOUN
fcis-22610	220	27	2020	2020	NUM
fcis-22610	220	28	.	.	PUNCT
fcis-22610	221	1	[	[	X
fcis-22610	221	2	13	13	NUM
fcis-22610	221	3	]	]	X
fcis-22610	221	4	ting	ting	PROPN
fcis-22610	221	5	chen	chen	PROPN
fcis-22610	221	6	,	,	PUNCT
fcis-22610	221	7	simon	simon	PROPN
fcis-22610	221	8	kornblith	kornblith	PROPN
fcis-22610	221	9	,	,	PUNCT
fcis-22610	221	10	mohammad	mohammad	PROPN
fcis-22610	221	11	norouzi	norouzi	PROPN
fcis-22610	221	12	,	,	PUNCT
fcis-22610	221	13	and	and	CCONJ
fcis-22610	221	14	geoffrey	geoffrey	PROPN
fcis-22610	221	15	hinton	hinton	PROPN
fcis-22610	221	16	.	.	PUNCT
fcis-22610	222	1	a	a	DET
fcis-22610	222	2	simple	simple	ADJ
fcis-22610	222	3	framework	framework	NOUN
fcis-22610	222	4	for	for	ADP
fcis-22610	222	5	contrastive	contrastive	ADJ
fcis-22610	222	6	learning	learning	NOUN
fcis-22610	222	7	of	of	ADP
fcis-22610	222	8	visual	visual	ADJ
fcis-22610	222	9	representations	representation	NOUN
fcis-22610	222	10	.	.	PUNCT
fcis-22610	223	1	arxiv	arxiv	PROPN
fcis-22610	223	2	preprint	preprint	NOUN
fcis-22610	223	3	,	,	PUNCT
fcis-22610	223	4	2020	2020	NUM
fcis-22610	223	5	.	.	PUNCT
fcis-22610	224	1	[	[	X
fcis-22610	224	2	14	14	NUM
fcis-22610	224	3	]	]	X
fcis-22610	224	4	he	he	PRON
fcis-22610	224	5	k	k	PROPN
fcis-22610	224	6	,	,	PUNCT
fcis-22610	224	7	zhang	zhang	PROPN
fcis-22610	224	8	x	x	X
fcis-22610	224	9	,	,	PUNCT
fcis-22610	224	10	ren	ren	PROPN
fcis-22610	224	11	s.	s.	PROPN
fcis-22610	224	12	deep	deep	ADJ
fcis-22610	224	13	residual	residual	ADJ
fcis-22610	224	14	learning	learning	NOUN
fcis-22610	224	15	for	for	ADP
fcis-22610	224	16	image	image	NOUN
fcis-22610	224	17	recognition[j].ieee	recognition[j].ieee	NOUN
fcis-22610	224	18	,	,	PUNCT
fcis-22610	224	19	2016	2016	NUM
fcis-22610	224	20	.	.	PUNCT
fcis-22610	225	1	[	[	X
fcis-22610	225	2	15	15	X
fcis-22610	225	3	]	]	X
fcis-22610	225	4	fouad	fouad	PROPN
fcis-22610	225	5	f	f	PROPN
fcis-22610	225	6	khalil	khalil	PROPN
fcis-22610	225	7	and	and	CCONJ
fcis-22610	225	8	pierre	pierre	PROPN
fcis-22610	225	9	payeur	payeur	PROPN
fcis-22610	225	10	.	.	PUNCT
fcis-22610	226	1	dexterous	dexterous	ADJ
fcis-22610	226	2	robotic	robotic	ADJ
fcis-22610	226	3	manipulation	manipulation	NOUN
fcis-22610	226	4	of	of	ADP
fcis-22610	226	5	deformable	deformable	ADJ
fcis-22610	226	6	objects	object	NOUN
fcis-22610	226	7	with	with	ADP
fcis-22610	226	8	the	the	DET
fcis-22610	226	9	multi	multi	ADJ
fcis-22610	226	10	-	-	ADJ
fcis-22610	226	11	sensory	sensory	ADJ
fcis-22610	226	12	feedback	feedback	NOUN
fcis-22610	226	13	-	-	PUNCT
fcis-22610	226	14	a	a	DET
fcis-22610	226	15	review	review	NOUN
fcis-22610	226	16	.	.	PUNCT
fcis-22610	227	1	in	in	ADP
fcis-22610	227	2	robot	robot	NOUN
fcis-22610	227	3	manipulators	manipulator	NOUN
fcis-22610	227	4	trends	trend	NOUN
fcis-22610	227	5	and	and	CCONJ
fcis-22610	227	6	development	development	NOUN
fcis-22610	227	7	.	.	PUNCT
fcis-22610	228	1	2010	2010	NUM
fcis-22610	228	2	.	.	PUNCT
fcis-22610	229	1	infogan	infogan	NOUN
fcis-22610	229	2	.	.	PUNCT
fcis-22610	230	1	in	in	ADP
fcis-22610	230	2	neurips	neurip	NOUN
fcis-22610	230	3	,	,	PUNCT
fcis-22610	230	4	2018	2018	NUM
fcis-22610	230	5	.	.	PUNCT
fcis-22610	231	1	[	[	X
fcis-22610	231	2	16	16	NUM
fcis-22610	231	3	]	]	X
fcis-22610	231	4	p	p	X
fcis-22610	231	5	jiménez	jiménez	PROPN
fcis-22610	231	6	.	.	PUNCT
fcis-22610	232	1	survey	survey	NOUN
fcis-22610	232	2	on	on	ADP
fcis-22610	232	3	model	model	NOUN
fcis-22610	232	4	-	-	PUNCT
fcis-22610	232	5	based	base	VERB
fcis-22610	232	6	manipulation	manipulation	NOUN
fcis-22610	232	7	planning	planning	NOUN
fcis-22610	232	8	of	of	ADP
fcis-22610	232	9	deformable	deformable	ADJ
fcis-22610	232	10	objects	object	NOUN
fcis-22610	232	11	.	.	PUNCT
fcis-22610	233	1	robotics	robotic	NOUN
fcis-22610	233	2	and	and	CCONJ
fcis-22610	233	3	computer	computer	NOUN
fcis-22610	233	4	-	-	PUNCT
fcis-22610	233	5	integrated	integrate	VERB
fcis-22610	233	6	manufacturing	manufacturing	NOUN
fcis-22610	233	7	,	,	PUNCT
fcis-22610	233	8	2012	2012	NUM
fcis-22610	233	9	.	.	PUNCT
fcis-22610	234	1	[	[	X
fcis-22610	234	2	17	17	NUM
fcis-22610	234	3	]	]	PUNCT
fcis-22610	234	4	mitul	mitul	NOUN
fcis-22610	234	5	saha	saha	PROPN
fcis-22610	234	6	and	and	CCONJ
fcis-22610	234	7	pekka	pekka	PROPN
fcis-22610	234	8	isto	isto	PROPN
fcis-22610	234	9	.	.	PUNCT
fcis-22610	235	1	manipulation	manipulation	NOUN
fcis-22610	235	2	planning	plan	VERB
fcis-22610	235	3	for	for	ADP
fcis-22610	235	4	deformable	deformable	ADJ
fcis-22610	235	5	linear	linear	ADJ
fcis-22610	235	6	objects	object	NOUN
fcis-22610	235	7	.	.	PUNCT
fcis-22610	236	1	in	in	ADP
fcis-22610	236	2	t	t	PROPN
fcis-22610	236	3	-	-	PUNCT
fcis-22610	236	4	ro	ro	NOUN
fcis-22610	236	5	,	,	PUNCT
fcis-22610	236	6	2007	2007	NUM
fcis-22610	236	7	.	.	PUNCT
fcis-22610	237	1	[	[	X
fcis-22610	237	2	18	18	NUM
fcis-22610	237	3	]	]	X
fcis-22610	237	4	hidefumi	hidefumi	NOUN
fcis-22610	237	5	wakamatsu	wakamatsu	NOUN
fcis-22610	237	6	,	,	PUNCT
fcis-22610	237	7	eiji	eiji	PROPN
fcis-22610	237	8	arai	arai	PROPN
fcis-22610	237	9	,	,	PUNCT
fcis-22610	237	10	and	and	CCONJ
fcis-22610	237	11	shinichi	shinichi	PROPN
fcis-22610	237	12	hirai	hirai	PROPN
fcis-22610	237	13	.	.	PUNCT
fcis-22610	238	1	knotting	knot	VERB
fcis-22610	238	2	/	/	SYM
fcis-22610	238	3	unknotting	unknotte	VERB
fcis-22610	238	4	manipulation	manipulation	NOUN
fcis-22610	238	5	of	of	ADP
fcis-22610	238	6	deformable	deformable	ADJ
fcis-22610	238	7	linear	linear	ADJ
fcis-22610	238	8	objects	object	NOUN
fcis-22610	238	9	.	.	PUNCT
fcis-22610	239	1	ijrr	ijrr	NOUN
fcis-22610	239	2	,	,	PUNCT
fcis-22610	239	3	2006	2006	NUM
fcis-22610	239	4	.	.	PUNCT
fcis-22610	240	1	[	[	X
fcis-22610	240	2	19	19	NUM
fcis-22610	240	3	]	]	PUNCT
fcis-22610	240	4	mark	mark	PROPN
fcis-22610	240	5	moll	moll	PROPN
fcis-22610	240	6	and	and	CCONJ
fcis-22610	240	7	lydia	lydia	PROPN
fcis-22610	240	8	e	e	PROPN
fcis-22610	240	9	kavraki	kavraki	PROPN
fcis-22610	240	10	.	.	PUNCT
fcis-22610	241	1	path	path	PROPN
fcis-22610	241	2	planning	planning	NOUN
fcis-22610	241	3	for	for	ADP
fcis-22610	241	4	deformable	deformable	ADJ
fcis-22610	241	5	linear	linear	ADJ
fcis-22610	241	6	objects	object	NOUN
fcis-22610	241	7	.	.	PUNCT
fcis-22610	242	1	t	t	PROPN
fcis-22610	242	2	-	-	PUNCT
fcis-22610	242	3	ro	ro	NOUN
fcis-22610	242	4	,	,	PUNCT
fcis-22610	242	5	2006	2006	NUM
fcis-22610	242	6	.	.	PUNCT
fcis-22610	243	1	[	[	X
fcis-22610	243	2	20	20	NUM
fcis-22610	243	3	]	]	X
fcis-22610	243	4	samuel	samuel	PROPN
fcis-22610	243	5	rodriguez	rodriguez	PROPN
fcis-22610	243	6	,	,	PUNCT
fcis-22610	243	7	xinyu	xinyu	PROPN
fcis-22610	243	8	tang	tang	PROPN
fcis-22610	243	9	,	,	PUNCT
fcis-22610	243	10	jyh	jyh	NOUN
fcis-22610	243	11	-	-	PUNCT
fcis-22610	243	12	ming	ming	NOUN
fcis-22610	243	13	lien	lien	NOUN
fcis-22610	243	14	,	,	PUNCT
fcis-22610	243	15	and	and	CCONJ
fcis-22610	243	16	nancy	nancy	PROPN
fcis-22610	243	17	m	m	PROPN
fcis-22610	243	18	amato	amato	PROPN
fcis-22610	243	19	.	.	PUNCT
fcis-22610	244	1	an	an	DET
fcis-22610	244	2	obstacle	obstacle	NOUN
fcis-22610	244	3	-	-	PUNCT
fcis-22610	244	4	based	base	VERB
fcis-22610	244	5	rapidly	rapidly	ADV
fcis-22610	244	6	-	-	PUNCT
fcis-22610	244	7	exploring	explore	VERB
fcis-22610	244	8	random	random	ADJ
fcis-22610	244	9	tree	tree	NOUN
fcis-22610	244	10	.	.	PUNCT
fcis-22610	245	1	in	in	ADP
fcis-22610	245	2	icra	icra	PROPN
fcis-22610	245	3	,	,	PUNCT
fcis-22610	245	4	2006	2006	NUM
fcis-22610	245	5	.	.	PUNCT
fcis-22610	246	1	[	[	X
fcis-22610	246	2	21	21	NUM
fcis-22610	246	3	]	]	X
fcis-22610	246	4	barbara	barbara	PROPN
fcis-22610	246	5	frank	frank	PROPN
fcis-22610	246	6	,	,	PUNCT
fcis-22610	246	7	cyrill	cyrill	NOUN
fcis-22610	246	8	stachniss	stachniss	NOUN
fcis-22610	246	9	,	,	PUNCT
fcis-22610	246	10	nichola	nichola	PROPN
fcis-22610	246	11	abdo	abdo	PROPN
fcis-22610	246	12	,	,	PUNCT
fcis-22610	246	13	and	and	CCONJ
fcis-22610	246	14	wolfram	wolfram	PROPN
fcis-22610	246	15	burgard	burgard	PROPN
fcis-22610	246	16	.	.	PUNCT
fcis-22610	247	1	efficient	efficient	ADJ
fcis-22610	247	2	motion	motion	NOUN
fcis-22610	247	3	planning	planning	NOUN
fcis-22610	247	4	for	for	ADP
fcis-22610	247	5	manipulation	manipulation	NOUN
fcis-22610	247	6	robots	robot	NOUN
fcis-22610	247	7	in	in	ADP
fcis-22610	247	8	environments	environment	NOUN
fcis-22610	247	9	with	with	ADP
fcis-22610	247	10	deformable	deformable	ADJ
fcis-22610	247	11	objects	object	NOUN
fcis-22610	247	12	.	.	PUNCT
fcis-22610	248	1	in	in	ADP
fcis-22610	248	2	iros	iro	NOUN
fcis-22610	248	3	,	,	PUNCT
fcis-22610	248	4	2011	2011	NUM
fcis-22610	248	5	.	.	PUNCT
fcis-22610	249	1	[	[	X
fcis-22610	249	2	22	22	NUM
fcis-22610	249	3	]	]	X
fcis-22610	249	4	grady	grady	PROPN
fcis-22610	249	5	williams	williams	PROPN
fcis-22610	249	6	,	,	PUNCT
fcis-22610	249	7	nolan	nolan	PROPN
fcis-22610	249	8	wagener	wagener	PROPN
fcis-22610	249	9	,	,	PUNCT
fcis-22610	249	10	brian	brian	PROPN
fcis-22610	249	11	goldfain	goldfain	NOUN
fcis-22610	249	12	,	,	PUNCT
fcis-22610	249	13	paul	paul	PROPN
fcis-22610	249	14	drews	drews	PROPN
fcis-22610	249	15	,	,	PUNCT
fcis-22610	249	16	james	james	PROPN
fcis-22610	249	17	m	m	PROPN
fcis-22610	249	18	rehg	rehg	PROPN
fcis-22610	249	19	,	,	PUNCT
fcis-22610	249	20	byron	byron	PROPN
fcis-22610	249	21	boots	boot	NOUN
fcis-22610	249	22	,	,	PUNCT
fcis-22610	249	23	and	and	CCONJ
fcis-22610	249	24	evangelos	evangelo	VERB
fcis-22610	249	25	a	a	DET
fcis-22610	249	26	theodorou	theodorou	NOUN
fcis-22610	249	27	.	.	PUNCT
fcis-22610	250	1	information	information	NOUN
fcis-22610	250	2	-	-	PUNCT
fcis-22610	250	3	theoretic	theoretic	NOUN
fcis-22610	250	4	mpc	mpc	NOUN
fcis-22610	250	5	for	for	ADP
fcis-22610	250	6	model	model	NOUN
fcis-22610	250	7	-	-	PUNCT
fcis-22610	250	8	based	base	VERB
fcis-22610	250	9	reinforcement	reinforcement	NOUN
fcis-22610	250	10	learning	learning	NOUN
fcis-22610	250	11	.	.	PUNCT
fcis-22610	251	1	in	in	ADP
fcis-22610	251	2	icra	icra	PROPN
fcis-22610	251	3	,	,	PUNCT
fcis-22610	251	4	2017	2017	NUM
fcis-22610	251	5	.	.	PUNCT
fcis-22610	252	1	[	[	X
fcis-22610	252	2	23	23	NUM
fcis-22610	252	3	]	]	X
fcis-22610	252	4	liangpeng	liangpeng	PROPN
fcis-22610	252	5	zhang	zhang	PROPN
fcis-22610	252	6	,	,	PUNCT
fcis-22610	252	7	ke	ke	PROPN
fcis-22610	252	8	tang	tang	PROPN
fcis-22610	252	9	,	,	PUNCT
fcis-22610	252	10	xin	xin	PROPN
fcis-22610	252	11	yao.explicit	yao.explicit	NOUN
fcis-22610	252	12	planning	plan	VERB
fcis-22610	252	13	for	for	ADP
fcis-22610	252	14	efficient	efficient	ADJ
fcis-22610	252	15	exploration	exploration	NOUN
fcis-22610	252	16	in	in	ADP
fcis-22610	252	17	reinforcement	reinforcement	NOUN
fcis-22610	252	18	learning	learning	NOUN
fcis-22610	252	19	-	-	PUNCT
fcis-22610	252	20	neurips	neurip	NOUN
fcis-22610	252	21	2019	2019	NUM
fcis-22610	252	22	.	.	PUNCT
fcis-22610	253	1	[	[	X
fcis-22610	253	2	24	24	NUM
fcis-22610	253	3	]	]	X
fcis-22610	253	4	dale	dale	PROPN
fcis-22610	253	5	mcconachie	mcconachie	PROPN
fcis-22610	253	6	,	,	PUNCT
fcis-22610	253	7	mengyao	mengyao	PROPN
fcis-22610	253	8	ruan	ruan	PROPN
fcis-22610	253	9	,	,	PUNCT
fcis-22610	253	10	and	and	CCONJ
fcis-22610	253	11	dmitry	dmitry	PROPN
fcis-22610	253	12	berenson	berenson	PROPN
fcis-22610	253	13	.	.	PUNCT
fcis-22610	253	14	interleaving	interleave	VERB
fcis-22610	253	15	planning	planning	NOUN
fcis-22610	253	16	and	and	CCONJ
fcis-22610	253	17	control	control	NOUN
fcis-22610	253	18	or	or	CCONJ
fcis-22610	253	19	deformable	deformable	ADJ
fcis-22610	253	20	object	object	NOUN
fcis-22610	253	21	manipulation	manipulation	NOUN
fcis-22610	253	22	.	.	PUNCT
fcis-22610	254	1	in	in	ADP
fcis-22610	254	2	international	international	ADJ
fcis-22610	254	3	symposium	symposium	NOUN
fcis-22610	254	4	on	on	ADP
fcis-22610	254	5	robotics	robotic	NOUN
fcis-22610	254	6	research	research	NOUN
fcis-22610	254	7	(	(	PUNCT
fcis-22610	254	8	isrr	isrr	PROPN
fcis-22610	254	9	)	)	PUNCT
fcis-22610	254	10	,	,	PUNCT
fcis-22610	254	11	2017	2017	NUM
fcis-22610	254	12	.	.	PUNCT
fcis-22610	255	1	[	[	X
fcis-22610	255	2	25	25	NUM
fcis-22610	255	3	]	]	PUNCT
fcis-22610	255	4	dmitry	dmitry	PROPN
fcis-22610	255	5	berenson	berenson	PROPN
fcis-22610	255	6	.	.	PUNCT
fcis-22610	256	1	manipulation	manipulation	NOUN
fcis-22610	256	2	of	of	ADP
fcis-22610	256	3	deformable	deformable	ADJ
fcis-22610	256	4	objects	object	NOUN
fcis-22610	256	5	without	without	ADP
fcis-22610	256	6	modeling	modeling	NOUN
fcis-22610	256	7	and	and	CCONJ
fcis-22610	256	8	simulating	simulate	VERB
fcis-22610	256	9	deformation	deformation	NOUN
fcis-22610	256	10	.	.	PUNCT
fcis-22610	257	1	in	in	ADP
fcis-22610	257	2	2013	2013	NUM
fcis-22610	257	3	ieee	ieee	NOUN
fcis-22610	257	4	/	/	SYM
fcis-22610	257	5	rsj	rsj	NOUN
fcis-22610	257	6	international	international	ADJ
fcis-22610	257	7	conference	conference	NOUN
fcis-22610	257	8	on	on	ADP
fcis-22610	257	9	intelligent	intelligent	ADJ
fcis-22610	257	10	robots	robot	NOUN
fcis-22610	257	11	and	and	CCONJ
fcis-22610	257	12	systems	system	NOUN
fcis-22610	257	13	pages	page	NOUN
fcis-22610	257	14	4525–4532	4525–4532	NOUN
fcis-22610	257	15	.	.	PUNCT
fcis-22610	257	16	ieee	ieee	PROPN
fcis-22610	257	17	,	,	PUNCT
fcis-22610	257	18	2013	2013	NUM
fcis-22610	257	19	.	.	PUNCT
fcis-22610	258	1	43	43	NUM
fcis-22610	259	1	[	[	X
fcis-22610	259	2	26	26	NUM
fcis-22610	259	3	]	]	X
fcis-22610	259	4	dale	dale	PROPN
fcis-22610	259	5	mcconachie	mcconachie	PROPN
fcis-22610	259	6	and	and	CCONJ
fcis-22610	259	7	dmitry	dmitry	PROPN
fcis-22610	259	8	berenson	berenson	PROPN
fcis-22610	259	9	.	.	PUNCT
fcis-22610	260	1	estimating	estimate	VERB
fcis-22610	260	2	model	model	NOUN
fcis-22610	260	3	utility	utility	NOUN
fcis-22610	260	4	for	for	ADP
fcis-22610	260	5	deformable	deformable	ADJ
fcis-22610	260	6	object	object	NOUN
fcis-22610	260	7	manipulation	manipulation	NOUN
fcis-22610	260	8	using	use	VERB
fcis-22610	260	9	multiarmed	multiarmed	ADJ
fcis-22610	260	10	bandit	bandit	NOUN
fcis-22610	260	11	methods	method	NOUN
fcis-22610	260	12	.	.	PUNCT
fcis-22610	261	1	ieee	ieee	NOUN
fcis-22610	261	2	transactions	transaction	NOUN
fcis-22610	261	3	on	on	ADP
fcis-22610	261	4	automation	automation	NOUN
fcis-22610	261	5	science	science	NOUN
fcis-22610	261	6	and	and	CCONJ
fcis-22610	261	7	engineering	engineering	NOUN
fcis-22610	261	8	,	,	PUNCT
fcis-22610	261	9	15(3):967–979	15(3):967–979	NUM
fcis-22610	261	10	,	,	PUNCT
fcis-22610	261	11	2018	2018	NUM
fcis-22610	261	12	.	.	PUNCT
fcis-22610	262	1	[	[	X
fcis-22610	262	2	27	27	NUM
fcis-22610	262	3	]	]	X
fcis-22610	262	4	david	david	PROPN
fcis-22610	262	5	navarro	navarro	PROPN
fcis-22610	262	6	-	-	PUNCT
fcis-22610	262	7	alarcon	alarcon	PROPN
fcis-22610	262	8	,	,	PUNCT
fcis-22610	262	9	y	y	PROPN
fcis-22610	262	10	un	un	PROPN
fcis-22610	262	11	-	-	PROPN
fcis-22610	262	12	hui	hui	PROPN
fcis-22610	262	13	liu	liu	PROPN
fcis-22610	262	14	,	,	PUNCT
fcis-22610	262	15	jose	jose	PROPN
fcis-22610	262	16	guadalupe	guadalupe	PROPN
fcis-22610	262	17	romero	romero	PROPN
fcis-22610	262	18	,	,	PUNCT
fcis-22610	262	19	and	and	CCONJ
fcis-22610	262	20	peng	peng	PROPN
fcis-22610	262	21	li	li	PROPN
fcis-22610	262	22	.	.	PROPN
fcis-22610	263	1	on	on	ADP
fcis-22610	263	2	the	the	DET
fcis-22610	263	3	visual	visual	ADJ
fcis-22610	263	4	deformation	deformation	NOUN
fcis-22610	263	5	serving	serve	VERB
fcis-22610	263	6	of	of	ADP
fcis-22610	263	7	compliant	compliant	ADJ
fcis-22610	263	8	objects	object	NOUN
fcis-22610	263	9	:	:	PUNCT
fcis-22610	263	10	uncalibrated	uncalibrated	ADJ
fcis-22610	263	11	control	control	NOUN
fcis-22610	263	12	methods	method	NOUN
fcis-22610	263	13	and	and	CCONJ
fcis-22610	263	14	experiments	experiment	NOUN
fcis-22610	263	15	.	.	PUNCT
fcis-22610	264	1	the	the	DET
fcis-22610	264	2	international	international	ADJ
fcis-22610	264	3	journal	journal	NOUN
fcis-22610	264	4	of	of	ADP
fcis-22610	264	5	robotics	robotic	NOUN
fcis-22610	264	6	research	research	NOUN
fcis-22610	264	7	,	,	PUNCT
fcis-22610	264	8	33(11):1462	33(11):1462	NUM
fcis-22610	264	9	–	–	PUNCT
fcis-22610	264	10	1480	1480	NUM
fcis-22610	264	11	,	,	PUNCT
fcis-22610	264	12	2014	2014	NUM
fcis-22610	264	13	.	.	PUNCT
fcis-22610	265	1	[	[	X
fcis-22610	265	2	28	28	NUM
fcis-22610	265	3	]	]	X
fcis-22610	265	4	nabil	nabil	PROPN
fcis-22610	265	5	essahbi	essahbi	PROPN
fcis-22610	265	6	,	,	PUNCT
fcis-22610	265	7	belhassen	belhassen	ADJ
fcis-22610	265	8	chedli	chedli	PROPN
fcis-22610	265	9	bouzgarrou	bouzgarrou	PROPN
fcis-22610	265	10	,	,	PUNCT
fcis-22610	265	11	and	and	CCONJ
fcis-22610	265	12	grigore	grigore	ADJ
fcis-22610	265	13	gogu	gogu	NOUN
fcis-22610	265	14	.	.	PUNCT
fcis-22610	266	1	soft	soft	ADJ
fcis-22610	266	2	material	material	NOUN
fcis-22610	266	3	modeling	modeling	NOUN
fcis-22610	266	4	for	for	ADP
fcis-22610	266	5	robotic	robotic	ADJ
fcis-22610	266	6	manipulation	manipulation	NOUN
fcis-22610	266	7	.	.	PUNCT
fcis-22610	267	1	in	in	ADP
fcis-22610	267	2	applied	applied	ADJ
fcis-22610	267	3	mechanics	mechanic	NOUN
fcis-22610	267	4	and	and	CCONJ
fcis-22610	267	5	materials	material	NOUN
fcis-22610	267	6	,	,	PUNCT
fcis-22610	267	7	volume	volume	NOUN
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fcis-22610	267	9	,	,	PUNCT
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fcis-22610	267	13	193	193	NUM
fcis-22610	267	14	.	.	PUNCT
fcis-22610	268	1	trans	trans	PROPN
fcis-22610	268	2	tech	tech	PROPN
fcis-22610	268	3	publ	publ	NOUN
fcis-22610	268	4	,	,	PUNCT
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fcis-22610	268	6	.	.	PUNCT
fcis-22610	269	1	[	[	X
fcis-22610	269	2	29	29	NUM
fcis-22610	269	3	]	]	X
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fcis-22610	269	5	finn	finn	PROPN
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fcis-22610	269	7	sergey	sergey	PROPN
fcis-22610	269	8	levine	levine	PROPN
fcis-22610	269	9	.	.	PUNCT
fcis-22610	270	1	deep	deep	ADJ
fcis-22610	270	2	visual	visual	ADJ
fcis-22610	270	3	foresight	foresight	NOUN
fcis-22610	270	4	for	for	ADP
fcis-22610	270	5	planning	planning	NOUN
fcis-22610	270	6	robotmotion	robotmotion	NOUN
fcis-22610	270	7	.	.	PUNCT
fcis-22610	271	1	in	in	ADP
fcis-22610	271	2	2017	2017	NUM
fcis-22610	271	3	ieee	ieee	NOUN
fcis-22610	271	4	international	international	ADJ
fcis-22610	271	5	conference	conference	NOUN
fcis-22610	271	6	on	on	ADP
fcis-22610	271	7	robotics	robotic	NOUN
fcis-22610	271	8	and	and	CCONJ
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fcis-22610	271	10	(	(	PUNCT
fcis-22610	271	11	icra	icra	PROPN
fcis-22610	271	12	)	)	PUNCT
fcis-22610	271	13	,	,	PUNCT
fcis-22610	271	14	pages	page	NOUN
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fcis-22610	271	16	.	.	PUNCT
fcis-22610	272	1	ieee	ieee	PROPN
fcis-22610	272	2	,	,	PUNCT
fcis-22610	272	3	2017	2017	NUM
fcis-22610	272	4	.	.	PUNCT
fcis-22610	273	1	[	[	X
fcis-22610	273	2	30	30	NUM
fcis-22610	273	3	]	]	PUNCT
fcis-22610	273	4	aaron	aaron	PROPN
fcis-22610	273	5	van	van	PROPN
fcis-22610	273	6	den	den	PROPN
fcis-22610	273	7	oord	oord	PROPN
fcis-22610	273	8	,	,	PUNCT
fcis-22610	273	9	yazhe	yazhe	PROPN
fcis-22610	273	10	li	li	PROPN
fcis-22610	273	11	,	,	PUNCT
fcis-22610	273	12	and	and	CCONJ
fcis-22610	273	13	oriol	oriol	PROPN
fcis-22610	273	14	vinyals	vinyal	NOUN
fcis-22610	273	15	.	.	PUNCT
fcis-22610	274	1	representation	representation	NOUN
fcis-22610	274	2	learning	learn	VERB
fcis-22610	274	3	with	with	ADP
fcis-22610	274	4	contrastive	contrastive	ADJ
fcis-22610	274	5	predictive	predictive	ADJ
fcis-22610	274	6	coding	coding	NOUN
fcis-22610	274	7	.	.	PUNCT
fcis-22610	275	1	in	in	ADP
fcis-22610	275	2	arxiv	arxiv	PROPN
fcis-22610	275	3	preprint	preprint	NOUN
fcis-22610	275	4	,	,	PUNCT
fcis-22610	275	5	2018	2018	NUM
fcis-22610	275	6	.	.	PUNCT
fcis-22610	276	1	[	[	X
fcis-22610	276	2	31	31	NUM
fcis-22610	276	3	]	]	X
fcis-22610	276	4	yonglong	yonglong	PROPN
fcis-22610	276	5	tian	tian	PROPN
fcis-22610	276	6	,	,	PUNCT
fcis-22610	276	7	dilip	dilip	PROPN
fcis-22610	276	8	krishnan	krishnan	PROPN
fcis-22610	276	9	,	,	PUNCT
fcis-22610	276	10	and	and	CCONJ
fcis-22610	276	11	phillip	phillip	PROPN
fcis-22610	276	12	isola	isola	PROPN
fcis-22610	276	13	.	.	PUNCT
fcis-22610	277	1	contrastive	contrastive	ADJ
fcis-22610	277	2	multiview	multiview	PROPN
fcis-22610	277	3	coding	coding	NOUN
fcis-22610	277	4	.	.	PUNCT
fcis-22610	278	1	arxiv	arxiv	PROPN
fcis-22610	278	2	preprint	preprint	NOUN
fcis-22610	278	3	,	,	PUNCT
fcis-22610	278	4	2019	2019	NUM
fcis-22610	278	5	.	.	PUNCT
fcis-22610	279	1	[	[	X
fcis-22610	279	2	32	32	NUM
fcis-22610	279	3	]	]	PUNCT
fcis-22610	279	4	josh	josh	PROPN
fcis-22610	279	5	tobin	tobin	PROPN
fcis-22610	279	6	,	,	PUNCT
fcis-22610	279	7	rachel	rachel	PROPN
fcis-22610	279	8	fong	fong	PROPN
fcis-22610	279	9	,	,	PUNCT
fcis-22610	279	10	alex	alex	PROPN
fcis-22610	279	11	ray	ray	PROPN
fcis-22610	279	12	,	,	PUNCT
fcis-22610	279	13	jonas	jonas	PROPN
fcis-22610	279	14	schneider	schneider	PROPN
fcis-22610	279	15	,	,	PUNCT
fcis-22610	279	16	wojciech	wojciech	PROPN
fcis-22610	279	17	zaremba	zaremba	NOUN
fcis-22610	279	18	,	,	PUNCT
fcis-22610	279	19	pieter	pieter	NOUN
fcis-22610	279	20	abbeel	abbeel	NOUN
fcis-22610	279	21	domain	domain	NOUN
fcis-22610	279	22	randomization	randomization	NOUN
fcis-22610	279	23	for	for	ADP
fcis-22610	279	24	transferring	transfer	VERB
fcis-22610	279	25	deep	deep	ADJ
fcis-22610	279	26	neural	neural	ADJ
fcis-22610	279	27	networks	network	NOUN
fcis-22610	279	28	from	from	ADP
fcis-22610	279	29	simulation	simulation	NOUN
fcis-22610	279	30	to	to	ADP
fcis-22610	279	31	the	the	DET
fcis-22610	279	32	real	real	ADJ
fcis-22610	279	33	world	world	NOUN
fcis-22610	279	34	in	in	ADP
fcis-22610	279	35	iros	iro	NOUN
fcis-22610	279	36	2017	2017	NUM
fcis-22610	279	37	.	.	PUNCT
fcis-22610	280	1	[	[	X
fcis-22610	280	2	33	33	NUM
fcis-22610	280	3	]	]	X
fcis-22610	280	4	kaiser	kaiser	PROPN
fcis-22610	280	5	,	,	PUNCT
fcis-22610	280	6	l.	l.	PROPN
fcis-22610	280	7	,	,	PUNCT
fcis-22610	280	8	babaeizadeh	babaeizadeh	PROPN
fcis-22610	280	9	,	,	PUNCT
fcis-22610	280	10	m.	m.	NOUN
fcis-22610	280	11	,	,	PUNCT
fcis-22610	280	12	milos	milos	PROPN
fcis-22610	280	13	,	,	PUNCT
fcis-22610	280	14	p.	p.	NOUN
fcis-22610	280	15	,	,	PUNCT
fcis-22610	280	16	osinski	osinski	PROPN
fcis-22610	280	17	,	,	PUNCT
fcis-22610	280	18	b.	b.	PROPN
fcis-22610	280	19	,	,	PUNCT
fcis-22610	280	20	campbell	campbell	PROPN
fcis-22610	280	21	,	,	PUNCT
fcis-22610	280	22	r.h	r.h	PROPN
fcis-22610	280	23	.	.	PROPN
fcis-22610	280	24	,	,	PUNCT
fcis-22610	280	25	&	&	CCONJ
fcis-22610	280	26	czechowski	czechowski	NOUN
fcis-22610	280	27	,	,	PUNCT
fcis-22610	280	28	k	k	PROPN
fcis-22610	280	29	..	..	PUNCT
fcis-22610	280	30	model	model	NOUN
fcis-22610	280	31	-	-	PUNCT
fcis-22610	280	32	based	base	VERB
fcis-22610	280	33	reinforcement	reinforcement	NOUN
fcis-22610	280	34	learning	learning	NOUN
fcis-22610	280	35	for	for	ADP
fcis-22610	280	36	atari.yuval	atari.yuval	PROPN
fcis-22610	280	37	tassa	tassa	PROPN
fcis-22610	280	38	,	,	PUNCT
fcis-22610	280	39	yotam	yotam	PROPN
fcis-22610	280	40	doron	doron	PROPN
fcis-22610	280	41	,	,	PUNCT
fcis-22610	280	42	alistair	alistair	PROPN
fcis-22610	280	43	muldal	muldal	PROPN
fcis-22610	280	44	,	,	PUNCT
fcis-22610	280	45	tom	tom	PROPN
fcis-22610	280	46	erez	erez	PROPN
fcis-22610	280	47	,	,	PUNCT
fcis-22610	280	48	yazheli	yazheli	PROPN
fcis-22610	280	49	,	,	PUNCT
fcis-22610	280	50	diego	diego	PROPN
fcis-22610	280	51	de	de	PROPN
fcis-22610	280	52	las	las	PROPN
fcis-22610	280	53	casas	casas	PROPN
fcis-22610	280	54	,	,	PUNCT
fcis-22610	280	55	david	david	PROPN
fcis-22610	280	56	budden	budden	PROPN
fcis-22610	280	57	,	,	PUNCT
fcis-22610	280	58	abbas	abbas	PROPN
fcis-22610	280	59	abdolmaleki	abdolmaleki	PROPN
fcis-22610	280	60	,	,	PUNCT
fcis-22610	280	61	josh	josh	PROPN
fcis-22610	280	62	merel	merel	PROPN
fcis-22610	280	63	,	,	PUNCT
fcis-22610	280	64	andrew	andrew	PROPN
fcis-22610	280	65	lefrancq	lefrancq	PROPN
fcis-22610	280	66	.	.	PUNCT
fcis-22610	281	1	deepmind	deepmind	PROPN
fcis-22610	281	2	control	control	PROPN
fcis-22610	281	3	suite	suite	PROPN
fcis-22610	281	4	.	.	PUNCT
fcis-22610	282	1	in	in	ADP
fcis-22610	282	2	arxiv	arxiv	PROPN
fcis-22610	282	3	preprint	preprint	NOUN
fcis-22610	282	4	,	,	PUNCT
fcis-22610	282	5	2018	2018	NUM
fcis-22610	282	6	.	.	PUNCT
fcis-22610	283	1	[	[	X
fcis-22610	283	2	34	34	NUM
fcis-22610	283	3	]	]	X
fcis-22610	283	4	emanuel	emanuel	PROPN
fcis-22610	283	5	todorov	todorov	PROPN
fcis-22610	283	6	,	,	PUNCT
fcis-22610	283	7	tom	tom	PROPN
fcis-22610	283	8	erez	erez	PROPN
fcis-22610	283	9	,	,	PUNCT
fcis-22610	283	10	and	and	CCONJ
fcis-22610	283	11	yuval	yuval	PROPN
fcis-22610	283	12	tassa	tassa	PROPN
fcis-22610	283	13	.	.	PUNCT
fcis-22610	284	1	mujoco	mujoco	PROPN
fcis-22610	284	2	:	:	PUNCT
fcis-22610	284	3	a	a	DET
fcis-22610	284	4	physics	physics	NOUN
fcis-22610	284	5	engine	engine	NOUN
fcis-22610	284	6	for	for	ADP
fcis-22610	284	7	model	model	NOUN
fcis-22610	284	8	-	-	PUNCT
fcis-22610	284	9	based	base	VERB
fcis-22610	284	10	control	control	NOUN
fcis-22610	284	11	.	.	PUNCT
fcis-22610	285	1	in	in	ADP
fcis-22610	285	2	iros	iro	NOUN
fcis-22610	285	3	,	,	PUNCT
fcis-22610	285	4	2012	2012	NUM
fcis-22610	285	5	.	.	PUNCT
fcis-22610	286	1	[	[	X
fcis-22610	286	2	35	35	NUM
fcis-22610	286	3	]	]	PUNCT
fcis-22610	286	4	danijar	danijar	PROPN
fcis-22610	286	5	hafner	hafner	PROPN
fcis-22610	286	6	,	,	PUNCT
fcis-22610	286	7	timothy	timothy	PROPN
fcis-22610	286	8	lillicrap	lillicrap	PROPN
fcis-22610	286	9	,	,	PUNCT
fcis-22610	286	10	ian	ian	PROPN
fcis-22610	286	11	fischer	fischer	PROPN
fcis-22610	286	12	,	,	PUNCT
fcis-22610	286	13	ruben	ruben	PROPN
fcis-22610	286	14	villegas	villegas	PROPN
fcis-22610	286	15	,	,	PUNCT
fcis-22610	286	16	david	david	PROPN
fcis-22610	286	17	ha	ha	INTJ
fcis-22610	286	18	,	,	PUNCT
fcis-22610	286	19	honglak	honglak	PROPN
fcis-22610	286	20	lee	lee	PROPN
fcis-22610	286	21	,	,	PUNCT
fcis-22610	286	22	and	and	CCONJ
fcis-22610	286	23	james	james	PROPN
fcis-22610	286	24	davidson	davidson	PROPN
fcis-22610	286	25	.	.	PUNCT
fcis-22610	287	1	learning	learn	VERB
fcis-22610	287	2	latent	latent	NOUN
fcis-22610	287	3	dynamics	dynamic	NOUN
fcis-22610	287	4	for	for	ADP
fcis-22610	287	5	planning	plan	VERB
fcis-22610	287	6	from	from	ADP
fcis-22610	287	7	pixels	pixel	NOUN
fcis-22610	287	8	.	.	PUNCT
fcis-22610	288	1	arxiv	arxiv	PROPN
fcis-22610	288	2	preprint	preprint	NOUN
fcis-22610	288	3	,	,	PUNCT
fcis-22610	288	4	2018	2018	NUM
fcis-22610	288	5	.	.	PUNCT
fcis-22610	289	1	[	[	X
fcis-22610	289	2	36	36	NUM
fcis-22610	289	3	]	]	X
fcis-22610	289	4	sascha	sascha	PROPN
fcis-22610	289	5	lange	lange	PROPN
fcis-22610	289	6	and	and	CCONJ
fcis-22610	289	7	martin	martin	PROPN
fcis-22610	289	8	riedmiller	riedmiller	NOUN
fcis-22610	289	9	.	.	PUNCT
fcis-22610	290	1	deep	deep	ADJ
fcis-22610	290	2	auto	auto	NOUN
fcis-22610	290	3	-	-	PUNCT
fcis-22610	290	4	encoder	encoder	NOUN
fcis-22610	290	5	neural	neural	ADJ
fcis-22610	290	6	networks	network	NOUN
fcis-22610	290	7	in	in	ADP
fcis-22610	290	8	reinforcement	reinforcement	NOUN
fcis-22610	290	9	learning	learning	NOUN
fcis-22610	290	10	.	.	PUNCT
fcis-22610	291	1	in	in	ADP
fcis-22610	291	2	ijcnn	ijcnn	PROPN
fcis-22610	291	3	,	,	PUNCT
fcis-22610	291	4	2010	2010	NUM
fcis-22610	291	5	[	[	X
fcis-22610	291	6	37	37	NUM
fcis-22610	291	7	]	]	X
fcis-22610	291	8	lukasz	lukasz	PROPN
fcis-22610	291	9	kaiser	kaiser	PROPN
fcis-22610	291	10	,	,	PUNCT
fcis-22610	291	11	mohammad	mohammad	PROPN
fcis-22610	291	12	babaeizadeh	babaeizadeh	PROPN
fcis-22610	291	13	,	,	PUNCT
fcis-22610	291	14	piotr	piotr	PROPN
fcis-22610	291	15	milos	milos	PROPN
fcis-22610	291	16	,	,	PUNCT
fcis-22610	291	17	blazej	blazej	NOUN
fcis-22610	291	18	osinski	osinski	PROPN
fcis-22610	291	19	,	,	PUNCT
fcis-22610	291	20	roy	roy	PROPN
fcis-22610	291	21	h	h	PROPN
fcis-22610	291	22	campbell	campbell	PROPN
fcis-22610	291	23	,	,	PUNCT
fcis-22610	291	24	konrad	konrad	PROPN
fcis-22610	291	25	czechowski	czechowski	PROPN
fcis-22610	291	26	,	,	PUNCT
fcis-22610	291	27	dumitru	dumitru	PROPN
fcis-22610	291	28	erhan	erhan	PROPN
fcis-22610	291	29	,	,	PUNCT
fcis-22610	291	30	chelsea	chelsea	PROPN
fcis-22610	291	31	finn	finn	PROPN
fcis-22610	291	32	,	,	PUNCT
fcis-22610	291	33	piotr	piotr	PROPN
fcis-22610	291	34	kozakowski	kozakowski	PROPN
fcis-22610	291	35	,	,	PUNCT
fcis-22610	291	36	sergey	sergey	PROPN
fcis-22610	291	37	levine	levine	PROPN
fcis-22610	291	38	.	.	PUNCT
fcis-22610	291	39	model	model	NOUN
fcis-22610	291	40	-	-	PUNCT
fcis-22610	291	41	based	base	VERB
fcis-22610	291	42	reinforcement	reinforcement	NOUN
fcis-22610	291	43	learning	learning	NOUN
fcis-22610	291	44	for	for	ADP
fcis-22610	291	45	atari	atari	NOUN
fcis-22610	291	46	.	.	PUNCT
fcis-22610	292	1	in	in	ADP
fcis-22610	292	2	arxiv	arxiv	PROPN
fcis-22610	292	3	preprint	preprint	NOUN
fcis-22610	292	4	,	,	PUNCT
fcis-22610	292	5	2019	2019	NUM
fcis-22610	292	6	.	.	PUNCT