Search is not available for this dataset
action list | observation.state list | observation.environment_state list | timestamp float32 | frame_index int64 | episode_index int64 | index int64 | task_index int64 |
|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
This dataset was created using LeRobot.
Dataset Description
dvla-can2k-fixed-250hz-events (250 fps native) - corrected grasp alignment
2,005 episodes / 1,805,397 frames at the native 250 Hz rate. Rolling-can place, one object (can11) into one of 5 receptacles (3 bowls + plate + tray). 20% static, 80% launched at 0.25-1.5 m/s.
What is "fixed" here. The earlier multi-object builds ran the intercept state machine with its alignment gate OFF (it skips the wait so it can catch fast objects) and derived the grasp yaw from the object's tumbling body axis. The wrist therefore closed on the object 20-35 deg misaligned on average, and up to 71 deg in the tail - the fingers pinched the rim instead of closing across the body. Those grasps still succeeded (the 80 mm gripper is far wider than a 50 mm can), so the misalignment survived the success filter and went into the labels.
This build restores the original DynamicVLA behaviour: the descent waits until the wrist is within 15 deg of the commanded grasp yaw, and that yaw comes from the motion direction (latched at descent entry) rather than the spinning body axis. Measured over all 2,005 episodes, the grasp-moment yaw error is median 3.1 deg, p90 5.4 deg, worst 18.4 deg - versus median 22.4 / p90 47.2 / worst 70.6 deg in the unfixed multi-object data. 100% of episodes are now inside 20 deg.
Six camera columns at 360x480: 3 RGB (opst_cam, side_cam, wrist_cam) + 3 DVS event columns
(*_events) from the v2e emulator, each event frame
accumulating 4 ms. Event frames are polarity renderings - turn off ImageNet normalization for them.
Rendering requires the env flags CAN_TEXTURE=1 BASKET_SCENE=1 TEXTURED_OBJS=1; the object meshes
are bundled with the 7-object dataset (assets/assets_bundle.tar.gz).
Task
MuJoCo / robosuite (Panda + OSC-or-IK) place: pick a can off the table and drop it into a
bowl. In 80% of the episodes the can is rolling when the episode starts (speed sampled from
0.25-0.75 m/s); the rest are static. Demonstrations come from a scripted state machine that
reads privileged simulator state, and only successful attempts are kept.
Layout
LeRobot v2.1, robot_type: panda, 3 RGB cameras at 360x480 (wrist_cam, side_cam,
opst_cam). Training uses opst_cam + wrist_cam only.
action(10,) =[dx, dy, dz, sin/cos of the 3 euler angles, gripper]observation.state(9,) =[x, y, z, sin/cos of the 3 euler angles]observation.environment_state(9,) = privileged object pose/velocity. Dropped during training (it is not available on a real robot).
action is a delta: the absolute target minus the state of that same frame. At evaluation
the policy output is decoded as target = live_state + predicted_delta, re-anchored every step.
The absolute target it encodes is a future-EE relabel: instead of the pose the state machine commanded at time t, the label is the pose the arm had actually reached 320 ms later. A sweep over that offset found it matters enormously - replaying the labels open-loop succeeds 20/20 at 320 ms but 5/20 at 200 ms and 0/20 at 40 ms.
-delta-trim additionally drops the leading frames where the arm is still holding position, so
that action[0] is a real motion rather than a placeholder (which otherwise causes a cold-start
stall).
meta/camera.jsonl maps each episode_index back to its source HDF5 filename. Conversion
shards round-robin, so episode order is not source order and this file is the only way back.
Reproducing
Generation, conversion, training and evaluation scripts, plus the measured results and the gotchas that cost the most time, are documented at https://github.com/mickeykang16/DynamicVLA/tree/mujoco.
- Homepage: https://github.com/mickeykang16/DynamicVLA/tree/mujoco
- Paper: [More Information Needed]
- License: apache-2.0
Dataset Structure
{
"codebase_version": "v2.1",
"robot_type": "panda",
"total_episodes": 2005,
"total_frames": 1805397,
"total_tasks": 15,
"total_videos": 12030,
"total_chunks": 3,
"chunks_size": 1000,
"fps": 250,
"splits": {
"train": "0:2005"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path": "videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4",
"features": {
"action": {
"dtype": "float32",
"shape": [
10
],
"names": [
"ee_pos_x",
"ee_pos_y",
"ee_pos_z",
"ee_rot_x_sin",
"ee_rot_x_cos",
"ee_rot_y_sin",
"ee_rot_y_cos",
"ee_rot_z_sin",
"ee_rot_z_cos",
"gripper"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
9
],
"names": [
"ee_pos_x",
"ee_pos_y",
"ee_pos_z",
"ee_rot_x_sin",
"ee_rot_x_cos",
"ee_rot_y_sin",
"ee_rot_y_cos",
"ee_rot_z_sin",
"ee_rot_z_cos"
]
},
"observation.environment_state": {
"dtype": "float32",
"shape": [
9
],
"names": [
"o0",
"o1",
"o2",
"o3",
"o4",
"o5",
"o6",
"o7",
"o8"
]
},
"observation.images.wrist_cam": {
"dtype": "video",
"shape": [
360,
480,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.height": 360,
"video.width": 480,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 250,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.side_cam": {
"dtype": "video",
"shape": [
360,
480,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.height": 360,
"video.width": 480,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 250,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.opst_cam": {
"dtype": "video",
"shape": [
360,
480,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.height": 360,
"video.width": 480,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 250,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.wrist_cam_events": {
"dtype": "video",
"shape": [
360,
480,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.height": 360,
"video.width": 480,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 250,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.side_cam_events": {
"dtype": "video",
"shape": [
360,
480,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.height": 360,
"video.width": 480,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 250,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.opst_cam_events": {
"dtype": "video",
"shape": [
360,
480,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.height": 360,
"video.width": 480,
"video.codec": "h264",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 250,
"video.channels": 3,
"has_audio": false
}
},
"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
}
}
}
Citation
BibTeX:
[More Information Needed]
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