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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](https://github.com/huggingface/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
[meta/info.json](meta/info.json):
```json
{
"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:**
```bibtex
[More Information Needed]
``` |