Search is not available for this dataset
observation.state listlengths 8 8 | action listlengths 8 8 | raw_command listlengths 9 9 | timestamp float32 0 275 | frame_index int64 0 5.5k | episode_index int64 0 487 | index int64 0 1.03M | task_index int64 0 0 |
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End of preview. Expand in Data Studio
This dataset was created using LeRobot.
Dataset Structure
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"video.fps": 20,
"video.channels": 3,
"has_audio": false,
"video.g": 5,
"video.crf": 23,
"video.preset": null,
"video.fast_decode": 0,
"video.video_backend": "pyav",
"video.extra_options": {},
"is_depth_map": false
}
},
"observation.state": {
"dtype": "float32",
"shape": [
8
],
"names": [
"panda_joint1",
"panda_joint2",
"panda_joint3",
"panda_joint4",
"panda_joint5",
"panda_joint6",
"panda_joint7",
"gripper"
]
},
"action": {
"dtype": "float32",
"shape": [
8
],
"names": [
"panda_joint1_target",
"panda_joint2_target",
"panda_joint3_target",
"panda_joint4_target",
"panda_joint5_target",
"panda_joint6_target",
"panda_joint7_target",
"gripper"
]
},
"raw_command": {
"dtype": "float32",
"shape": [
9
],
"names": [
"cmd_0",
"cmd_1",
"cmd_2",
"cmd_3",
"cmd_4",
"cmd_5",
"cmd_6",
"cmd_7",
"cmd_8"
]
},
"timestamp": {
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"shape": [
1
],
"names": null
},
"frame_index": {
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"shape": [
1
],
"names": null
},
"episode_index": {
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"shape": [
1
],
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},
"index": {
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"names": null
},
"task_index": {
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}
},
"total_episodes": 488,
"total_frames": 1028417,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 42,
"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": "packing.clear_organic_objects.franka.joint",
"splits": {
"train": "0:488"
}
}
Citation
BibTeX:
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About this dataset
Simulated Franka Panda demonstrations of a produce-clearing task (pick every organic item — lemons, a lime, an orange, a small pumpkin — off a cluttered packing table and drop it into the crate, leaving the distractors), generated in Isaac Sim by the CoSiGen data-generation pipeline from a scripted IK expert. Every episode is a certified success of the task grader (all organics in the crate, no distractor in it). Grasps are real friction pinches (no weld).
- Episodes / frames: 488 / 1,028,417 at 20 Hz (14.3 h); 4-5 items per episode, ~90-190 s each
- Cameras:
front,side,wrist- 480x640, h264 - State (8):
panda_joint1..7measured positions (rad) + gripper closedness in [0, 1] (0 = open) - Action (8): ABSOLUTE
panda_joint1..7_target(rad) latched at 20 Hz + commanded gripper closedness. Actions are the controller-latched commands the expert issued (native 20 Hz), so labels equal executed commands. - Control law: stationary — the scene's stock Franka joint PD and gripper drives; a policy trained on this data
deploys through the plain 20 Hz joint-target executor (
meta/bake.json->controller). - Randomization: the binding's own layout randomization (item subset 4-5 of 5, shuffle, jitter, yaw), per-episode physical params (produce static friction 0.90-1.30, dynamic 0.80-1.10), and 6 expert-parameter bands (hover / carry / release heights, first-attempt pinch height and jaw azimuth, drop-slot spread). The expert retries failed grasps, so episodes contain recoveries (re-grasps, a shake to free a wedged lemon).
- Idle filter: stationary ticks dropped at bake time (0).
- Extra column:
raw_command(9) - the raw controller command vector, not a policy input.
Task string: "identify every ORGANIC item — the fruits and vegetables — and place each one into the bin, leaving all the non-food clutter where it is. An item counts only when it is resting inside the bin; the job is done when every fruit and vegetable is in the bin and no non-food item has been put in."
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