Datasets:
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observation.state list | observation.environment_state list | action list | timestamp float32 0 23.4 | frame_index int64 0 351 | episode_index int64 0 99 | index int64 0 31.7k | task_index int64 0 0 |
|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
This dataset was created using LeRobot.
Dataset Description
⚠️ Read before mixing this with other DecentVLA data
This is a
Franka Pandain simulation, driven by a motion planner. Every other dataset in this organisation is SO-101 hardware, teleoperated by a human. Two things therefore differ, and neither is cosmetic:
- The embodiment. A 7-DoF Franka arm, not a 5-DoF SO-101. Different kinematics, different joint count, different workspace.
- The action space.
actionhere is 8-D — 7 arm joints plus a gripper — and its values are normalised joint targets emitted by a planner. It is not the same quantity as an SO-101 teleop action and the two are not interchangeable.Concatenating these with the SO-101 sets as if they shared an embodiment will train on incompatible action vectors. That is worth checking deliberately rather than discovering in a loss curve.
Blue cube on Green cube, Franka Panda in Isaac Sim, reset distribution right.
Put the blue cube on top of the green cube.
How the actions were produced
These are not teleoperated demonstrations. Every action is the output of a
motion planner discovered by GEPA, an LLM-driven program search, and then
frozen: the planner named cubestack_fast was promoted at
1.026 train / 0.937 held-out
and was not tuned, retrained or edited to produce this dataset. It is the same
planner across all twelve datasets in this collection, which is the point — the
datasets differ in the scene, never in the policy that acted in it.
The reset distribution
The two cubes spawn in the -y half of the region each one's ROLE was searched over, so the whole episode is displaced toward the arm's right relative to its twin. The halves are subsets of the region the planner was evolved on, which is what lets it be recorded rather than re-tuned.
| role | colour | spawn box, metres (x, y, z) |
|---|---|---|
| moved | blue | [0.42, -0.16, 0.0235] to [0.58, -0.11, 0.0235] |
| base | green | [0.42, 0.06, 0.0235] to [0.58, 0.11, 0.0235] |
observation.environment_state carries both cubes as (xyz + quaternion) in
the robot base frame, ordered ['moved', 'base'].
Note that the moved cube's y converges toward the base cube's over an episode — it is carried onto it. The reset distribution is the first frame of each episode; that is what distinguishes this dataset from its twin.
Contents
| episodes | 100 |
| frames | 31,660 |
| fps | 15 |
| success rate | 1.000 (100 of 100 attempts) |
| cameras | observation.images.overhead, observation.images.wrist, 480x640 |
| state / action | 8-D — 7 arm joints + gripper |
| scene | cubestack_yminus (e4e1f7ee2df938d9) |
Successful episodes only. Failed attempts are not included and are not padding: the count above is the number of episodes in which the cube was placed and stayed placed after the arm returned home.
- Homepage: [More Information Needed]
- Paper: [More Information Needed]
- License: apache-2.0
Dataset Structure
{
"codebase_version": "v3.0",
"fps": 15,
"features": {
"observation.images.overhead": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.fps": 15,
"video.channels": 3,
"has_audio": false,
"video.g": 2,
"video.crf": 30,
"video.preset": 12,
"video.fast_decode": 0,
"video.video_backend": "pyav",
"video.extra_options": {},
"is_depth_map": false
}
},
"observation.images.wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.fps": 15,
"video.channels": 3,
"has_audio": false,
"video.g": 2,
"video.crf": 30,
"video.preset": 12,
"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"
]
},
"observation.environment_state": {
"dtype": "float32",
"shape": [
14
],
"names": [
"moved.x",
"moved.y",
"moved.z",
"moved.qw",
"moved.qx",
"moved.qy",
"moved.qz",
"base.x",
"base.y",
"base.z",
"base.qw",
"base.qx",
"base.qy",
"base.qz"
]
},
"action": {
"dtype": "float32",
"shape": [
8
],
"names": [
"panda_joint1",
"panda_joint2",
"panda_joint3",
"panda_joint4",
"panda_joint5",
"panda_joint6",
"panda_joint7",
"gripper"
]
},
"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
}
},
"total_episodes": 100,
"total_frames": 31660,
"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": "franka",
"splits": {
"train": "0:100"
}
}
Citation
BibTeX:
[More Information Needed]
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