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sequencelengths 6
6
| observation.state
sequencelengths 6
6
| timestamp
float64 0
9.93
| task_index
int64 0
0
| episode_index
int64 0
104
| frame_index
int64 0
298
| index
int64 0
15.5k
|
|---|---|---|---|---|---|---|
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0.34369561076164246,
-0.08899261057376862,
1.0679113864898682,
1.2719806432724,
-0.0030687106773257256
] | 3.2 | 0 | 0 | 96 | 96 |
[
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0.2761839695463556,
-0.06137421545474568,
1.081720547389893,
1.296530301481501,
-0.004603066159105928
] | [
-0.19639748334884644,
0.32374897599220276,
-0.08132083714008331,
1.0633082389831543,
1.2888585329055786,
-0.004603066015988588
] | 3.233333 | 0 | 0 | 97 | 97 |
[
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0.17031344788691932,
-0.03529017388647879,
1.146163473617376,
1.3011333676406087,
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-0.20100055634975433,
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-0.06137421727180481,
1.0817205905914307,
1.2965302467346191,
-0.004603066015988588
] | 3.266667 | 0 | 0 | 98 | 98 |
[
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0.019946620022792322,
1.1814536475038546,
1.3195456322770325,
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0.17031344771385193,
-0.035290174186229706,
1.1461634635925293,
1.301133394241333,
-0.004603066015988588
] | 3.3 | 0 | 0 | 99 | 99 |
End of preview. Expand
in Data Studio
GetTheRubberNextG2
This dataset was generated using a phospho starter pack.
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.
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