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sequencelengths 6
6
| observation.state
sequencelengths 6
6
| timestamp
float64 0
9.7
| task_index
int64 0
0
| episode_index
int64 0
30
| frame_index
int64 0
291
| index
int64 0
7.03k
| observation.environment_state
sequencelengths 6
6
|
|---|---|---|---|---|---|---|---|
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-0.350161075592041,
0.514069139957428,
-0.016172094270586967,
-0.4583081007003784,
0.20931082963943481,
-2.5148262930385097e-19
] | 3.3 | 0 | 0 | 99 | 99 | [
0.018255800008773804,
-0.018144167959690094,
0.03370402753353119,
0.011066446080803871,
-0.03924114257097244,
0.10207168757915497
] |
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3_same_cameras
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.
We add an observation.environment_state column to the parquets. This has been computed by using PaliGemma and fast3r to determine object spaces in the scene and determine their positions relative to each other.
observation.environment_state is an array of size 6:
- The first 3 values are x,y,z for the position_item_relative_to_robot
- The last 3 values are x,y,z for the position_target_relative_to_robot
These have been estimated in 3D space
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