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pick banana from white bowl
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pick banana from white bowl
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pick banana from white bowl
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0
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0
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0
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pick banana from white bowl
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1
3
0
3
0
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0
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0
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0
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0
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0
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0
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0
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pick banana from white bowl
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1.333333
4
0
4
0
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0
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0
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0
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0
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0
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0
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0
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pick banana from white bowl
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1.666667
5
0
5
0
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[ -0.20000000298023224, -0.20000000298023224, -0.13124999403953552 ]
0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.125, 0.375, -0.125 ]
0
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0
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0
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[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
[ 0.5854832530021667, -0.11274364590644836, 0.9933878183364868, -0.5849275588989258, 0.6871814131736755, 0.010587270371615887, 0.4307311773300171 ]
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2
6
0
6
0
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0
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0
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0
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0
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pick banana from white bowl
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2.333333
7
0
7
0
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0
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0
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0
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0
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0
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0
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0
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pick banana from white bowl
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2.666667
8
0
8
0
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0
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0
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0
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0
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0
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0
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0
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pick banana from white bowl
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3
9
0
9
0
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0
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0
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0
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0
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0
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0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
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3.333333
10
0
10
0
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0
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0
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0
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0
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0
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pick banana from white bowl
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3.666667
11
0
11
0
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0
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0
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0
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0
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0
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[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
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4
12
0
12
0
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0
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0
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0
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0
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0
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[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
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4.333333
13
0
13
0
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[ -0.20000000298023224, -0.20000000298023224, -0.13124999403953552 ]
0
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0
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[ -0.125, 0.375, -0.125 ]
0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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0
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[ 0.3897761404514313, 0.24002429842948914, 0.5985221266746521, -0.4131646454334259, 0.6768477559089661, -0.6070429682731628, -0.05168088898062706 ]
[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ 0.5397905111312866, 0.09000857919454575, 0.5985599160194397, -0.41260120272636414, 0.6771460771560669, -0.6070318222045898, -0.05240021273493767 ]
[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
[ 0.6897790431976318, -0.059977997094392776, 0.8985223174095154, 0.676902174949646, -0.6069632172584534, -0.05173547565937042, -0.41318580508232117 ]
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4.666667
14
0
14
0
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pick banana from white bowl
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5
15
0
15
0
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0
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0
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0
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0
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pick banana from white bowl
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5.333333
16
0
16
0
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0
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0
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0
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0
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0
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0
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0
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pick banana from white bowl
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5.666667
17
0
17
0
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0
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0
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0
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0
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0
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0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
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6
18
0
18
0
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0
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0
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0
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0
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0
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0
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0
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pick banana from white bowl
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6.333333
19
0
19
0
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0
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0
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0
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0
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0
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[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
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6.666667
20
0
20
0
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[ -0.20000000298023224, -0.20000000298023224, -0.13124999403953552 ]
0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.125, 0.375, -0.125 ]
0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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0
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[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ 0.5741841197013855, 0.09298135340213776, 0.5386800765991211, -0.36022019386291504, 0.6825395822525024, -0.6334558129310608, -0.05581076070666313 ]
[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
[ 0.7241677641868591, -0.05700862035155296, 0.8386424779891968, 0.6823406219482422, -0.6333818435668945, -0.05513247102499008, -0.3608311116695404 ]
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7
21
0
21
0
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0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.125, 0.375, -0.125 ]
0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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0
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[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
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7.333333
22
0
22
0
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0
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0
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0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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0
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[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
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7.666667
23
0
23
0
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0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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[ -0.125, 0.375, -0.125 ]
0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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[ 0, 0.6000000238418579, -0.30000001192092896 ]
0
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[ 0.39449405670166016, 0.24715766310691833, 0.6284940242767334, -0.3870779871940613, 0.691996693611145, -0.6058571338653564, -0.06517919152975082 ]
[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ 0.059803158044815063, 2.630847215652466, 3.436838150024414, 1.6010844707489014, -4.005549430847168, 3.7116124629974365, 0.800000011920929 ]
[ 0.5445254445075989, 0.09713935106992722, 0.6285572052001953, -0.3860909342765808, 0.6924612522125244, -0.6058629155158997, -0.0660402849316597 ]
[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick banana from white bowl
[ 0.6944975256919861, -0.052844732999801636, 0.9284963607788086, 0.692062258720398, -0.6057855486869812, -0.06523682922124863, -0.3870631456375122 ]
[ 0.04839834198355675, 0.5640235543251038, 0.41982731223106384, 0.6236146092414856, 0.26226717233657837, 1.3434245586395264, -1.3421128988265991, 0.800000011920929 ]
[ 0.6944970488548279, -0.05284158140420914, 0.9284964799880981, -0.38708528876304626, 0.6919944286346436, -0.6058545112609863, -0.06518443673849106 ]
8
24
0
24
0
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[ -0.20000000298023224, -0.20000000298023224, -0.13124999403953552 ]
0
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[ -1.8120899200439453, -1.5773154497146606, 0.6932647228240967, -0.9431962370872498, -1.3659948110580444, 1.610842227935791, 0 ]
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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[ 0.3130338490009308, 0.15831206738948822, 0.8508326411247253, 0.16192366182804108, -0.6025059819221497, 0.7807638645172119, -0.03427819162607193 ]
[ -0.125, 0.375, -0.125 ]
0
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[ 0.43646231293678284, 0.0832667127251625, 0.6709514260292053, 0.23351037502288818, -0.5909528136253357, 0.7691750526428223, -0.0679517611861229 ]
[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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0
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[ 0.13813717663288116, 0.08334026485681534, 0.6761168241500854, 0.15903937816619873, -0.6032764911651611, 0.7807579040527344, -0.03436637297272682 ]
[ -0.30000001192092896, 0.30000001192092896, -0.30000001192092896 ]
0
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[ 0.48544713854789734, 3.2004873752593994, 3.8674914836883545, 1.2427456378936768, -3.144592046737671, 3.1362152099609375, 0 ]
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[ -0.15000000596046448, 0.15000000596046448, -0.30000001192092896 ]
0
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pick apple
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0
0
1
25
1
[ -1.5363600254058838, -0.9360352158546448, -0.6550309062004089, 0.08564076572656631, 1.1097105741500854, 2.530944347381592, -2.344031810760498, 0 ]
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0
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[ 0, 0.30000001192092896, -0.30000001192092896 ]
0
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pick apple
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pick apple
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3.333333
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1
35
1
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fractal20220817_data_train_20000_22500_augmented

Overview

  • Codebase version: v3.0
  • Robots: images, jaco, kinova3, kuka_iiwa, panda, sawyer, ur5e, xarm7
  • FPS: 3
  • Episodes: 2,500
  • Frames: 106,287
  • Splits:
    • train: 0:2500

Data Layout

data_path : data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet
video_path: videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4

Features

Feature dtype shape description
observation.images.image video 256×320×3 Source robot's image from original dataset
observation.images.jaco video 256×320×3 Augmented image for jaco robot
observation.images.kinova3 video 256×320×3 Augmented image for kinova3 robot
observation.images.kuka_iiwa video 256×320×3 Augmented image for kuka_iiwa robot
observation.images.panda video 256×320×3 Augmented image for panda robot
observation.images.sawyer video 256×320×3 Augmented image for sawyer robot
observation.images.ur5e video 256×320×3 Augmented image for ur5e robot
observation.images.xarm7 video 256×320×3 Augmented image for xarm7 robot
episode_index int64 1 Index of the current episode within the dataset.
frame_index int64 1 Index of the current frame within its episode.
index int64 1 Global frame index across the whole dataset.
natural_language_instruction string 1 Natural language command describing the task
observation.ee_pose float32 7 Source robot's eef position
observation.jaco.base_orientation float32 1 Rotation along z-axis CCW to make the robot not blocking the camera (mostly 0)
observation.jaco.base_position float32 3 Base translation applied so the trajectory remains achievable
observation.jaco.ee_error float32 7 The eef difference between the augmented jaco robot and the original robot
observation.jaco.ee_pose float32 7 The eef position of jaco robot
observation.jaco.joints float32 7 The joint position of jaco robot
observation.joints float32 8 Joint angle of source robot
observation.kinova3.base_orientation float32 1 Rotation along z-axis CCW to make the robot not blocking the camera (mostly 0)
observation.kinova3.base_position float32 3 Base translation applied so the trajectory remains achievable
observation.kinova3.ee_error float32 7 The eef difference between the augmented kinova3 robot and the original robot
observation.kinova3.ee_pose float32 7 The eef position of kinova3 robot
observation.kinova3.joints float32 8 The joint position of kinova3 robot
observation.kuka_iiwa.base_orientation float32 1 Rotation along z-axis CCW to make the robot not blocking the camera (mostly 0)
observation.kuka_iiwa.base_position float32 3 Base translation applied so the trajectory remains achievable
observation.kuka_iiwa.ee_error float32 7 The eef difference between the augmented kuka_iiwa robot and the original robot
observation.kuka_iiwa.ee_pose float32 7 The eef position of kuka_iiwa robot
observation.kuka_iiwa.joints float32 8 The joint position of kuka_iiwa robot
observation.panda.base_orientation float32 1 Rotation along z-axis CCW to make the robot not blocking the camera (mostly 0)
observation.panda.base_position float32 3 Base translation applied so the trajectory remains achievable
observation.panda.ee_error float32 7 The eef difference between the augmented panda robot and the original robot
observation.panda.ee_pose float32 7 The eef position of panda robot
observation.panda.joints float32 8 The joint position of panda robot
observation.sawyer.base_orientation float32 1 Rotation along z-axis CCW to make the robot not blocking the camera (mostly 0)
observation.sawyer.base_position float32 3 Base translation applied so the trajectory remains achievable
observation.sawyer.ee_error float32 7 The eef difference between the augmented sawyer robot and the original robot
observation.sawyer.ee_pose float32 7 The eef position of sawyer robot
observation.sawyer.joints float32 8 The joint position of sawyer robot
observation.state float32 7 Copy of the state field in source robot's RLDS dataset
observation.ur5e.base_orientation float32 1 Rotation along z-axis CCW to make the robot not blocking the camera (mostly 0)
observation.ur5e.base_position float32 3 Base translation applied so the trajectory remains achievable
observation.ur5e.ee_error float32 7 The eef difference between the augmented ur5e robot and the original robot
observation.ur5e.ee_pose float32 7 The eef position of ur5e robot
observation.ur5e.joints float32 7 The joint position of ur5e robot
observation.xarm7.base_orientation float32 1 Rotation along z-axis CCW to make the robot not blocking the camera (mostly 0)
observation.xarm7.base_position float32 3 Base translation applied so the trajectory remains achievable
observation.xarm7.ee_error float32 7 The eef difference between the augmented xarm7 robot and the original robot
observation.xarm7.ee_pose float32 7 The eef position of xarm7 robot
observation.xarm7.joints float32 8 The joint position of xarm7 robot
task_index int64 1 Integer ID of the high-level task this episode/frame belongs to.
timestamp float32 1 Time stamp of the current frame within the episode (in second).

Website

Paper

Citation Policy

If you use OXE-AugE datasets, please cite both our dataset and the upstream datasets.

Upstream Dataset Citation (original dataset)

@misc{googleblog_rt1_2022,
  author       = {Keerthana Gopalakrishnan and Kanishka Rao},
  title        = {RT-1: Robotics Transformer for real-world control at scale},
  howpublished = {Google Research Blog},
  year         = {2022},
  month        = {December},
  url          = {https://research.google/blog/rt-1-robotics-transformer-for-real-world-control-at-scale/}
}

OXE-AugE Dataset Citation (ours)

@misc{
  ji2025oxeauge,
  title  = {OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning},
  author = {Ji, Guanhua and Polavaram, Harsha and Chen, Lawrence Yunliang and Bajamahal, Sandeep and Ma, Zehan and Adebola, Simeon and Xu, Chenfeng and Goldberg, Ken},
  journal={arXiv preprint arXiv:2512.13100},
  year={2025}
}
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