SteveNguyen/sugarcube_in_mug_chunkrel_pi05_step20000
Robotics • 4B • Updated • 23
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action listlengths 8 8 | is_lost float32 0 0 | timestamp float32 0 10.7 | frame_index int64 0 535 | episode_index int64 0 149 | index int64 0 23k | task_index int64 0 0 | observation.state listlengths 2 2 |
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End of preview. Expand in Data Studio
This dataset was created using LeRobot.
Dataset Structure
{
"codebase_version": "v3.0",
"fps": 50,
"features": {
"observation.images.cam0": {
"dtype": "video",
"shape": [
3,
360,
480
],
"names": [
"channels",
"height",
"width"
],
"info": {
"video.height": 360,
"video.width": 480,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 50,
"video.channels": 3,
"has_audio": false
}
},
"action": {
"dtype": "float32",
"shape": [
8
],
"names": [
"x",
"y",
"z",
"ax",
"ay",
"az",
"strategy",
"closure"
]
},
"is_lost": {
"dtype": "float32",
"shape": [
1
],
"names": [
"right_is_lost"
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"observation.state": {
"dtype": "float32",
"shape": [
2
],
"names": [
"strategy",
"closure"
]
}
},
"total_episodes": 150,
"total_frames": 23046,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"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",
"robot_type": "grabette",
"splits": {
"train": "0:150"
}
}
Citation
BibTeX:
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Grabette notes
Gripper channels are (strategy, closure), not joint angles: the last two
channels of action and observation.state are normalised to the gripper's
reachable travel. In action, closure = 1.0 means "drive fully closed" and
the object stops the fingers; in observation.state closure stays continuous,
so "am I holding something" remains observable. The exact calibration is in
meta/grasp_projection.json.
action is 8D absolute pose [x, y, z, ax, ay, az, strategy, closure] —
deliberately not per-step deltas. meta/stats.json carries
action_relative_meta, so the action stats describe the chunk-relative
distribution (chunk_size 50) rather than the absolute one. Training must set
GRABETTE_CHUNK_RELATIVE=1; integrations/Pi05/train.py refuses otherwise
rather than normalise with the wrong scale.
Prepared from chouziel/grabette-sugar-cup-2008 to be channel-for-channel comparable with chouziel/grabette-sugar-cup-2008_graspproj (the per-step-delta baseline): the projected gripper channels are bit-identical across all 23046 frames, so an A/B between them isolates the action representation.
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Models trained or fine-tuned on SteveNguyen/sugarcube_in_mug_chunkrel