robot_type string | codebase_version string | total_episodes int64 | total_frames int64 | total_tasks int64 | total_videos int64 | total_chunks int64 | chunks_size int64 | fps int64 | splits dict | data_path string | video_path string | features dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
so-100 | v2.1 | 0 | 0 | 1 | 0 | 1 | 1,000 | 30 | {
"train": "0:0"
} | data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet | videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4 | {
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6"
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
} |
PICK
This dataset was generated using phosphobot.
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.
To get started in robotics, get your own phospho starter pack..
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