license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- mujoco
- robosuite
- manipulation
- event-camera
- dvs
configs:
- config_name: default
data_files: data/*/*.parquet
This dataset was created using LeRobot.
Dataset Description
dvla-can-250hz-events (250 fps native) - event frames accumulating 4 ms each
1016 episodes / 1,356,080 frames at the native 250 Hz rate. Same v2e settings as the 250->25 fps variant, but kept at full rate: each event frame covers 4 ms, so the polarity images are sparser (roughly a third of the pixels of the 40 ms version).
Two extra video columns on top of the 3 RGB cameras:
observation.images.opst_cam_events, observation.images.wrist_cam_events.
Note on encoding: SVT-AV1 refuses frame rates above 240 fps, so every video column here is H.264 rather than AV1. The 25 fps variants use AV1 for the base and H.264 after the trim re-encode.
The 250 Hz RGB counterpart of this dataset trains to only 36% place success versus 61% at 25 Hz, and the deficit is entirely on moving cans (static cans stay at 100%). This dataset exists to test whether event input recovers that gap.
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": 1016,
"total_frames": 1356080,
"total_tasks": 3,
"total_videos": 5080,
"total_chunks": 2,
"chunks_size": 1000,
"fps": 250,
"splits": {
"train": "0:1016"
},
"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.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]