Search is not available for this dataset
action listlengths 10 10 | observation.state listlengths 9 9 | observation.environment_state listlengths 9 9 | timestamp float32 0 8.35 | frame_index int64 0 2.09k | episode_index int64 0 1.02k | index int64 0 1.36M | task_index int64 0 2 |
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End of preview. Expand in Data Studio
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.
Raw event streams (raw_events/)
The event video columns are rasterised frames: where several events land on the same pixel
only one survives, discarding 72-73% of them. The stream before that rasterisation ships in this
repo under raw_events/ (1016 files, 20 GB).
Two arrays per camera:
opst_cam_events_xytp (N, 4) int32 one event per row: [x, y, t_us, polarity]
opst_cam_events_idx (T+1,) int32 frame boundaries
wrist_cam_events_xytp (M, 4) int32
wrist_cam_events_idx (T+1,) int32
x 0-479, y 0-359, polarity +1 (brighter) / -1 (darker), sorted by time.
Events of frame i are xytp[idx[i] : idx[i+1]]; idx[-1] equals the total event count.
Roughly 1.3-1.7 M events per episode for opst_cam, 4-8 M for wrist_cam.
import json, h5py
cam = {json.loads(l)["episode_index"]: json.loads(l)["filename"]
for l in open(f"{root}/meta/camera.jsonl")} # episode -> source filename
with h5py.File(f"{root}/raw_events/{cam[ep]}", "r") as h:
idx = h["opst_cam_events_idx"][:] # small, read whole
s, e = int(idx[frame]), int(idx[frame + span]) # span frames = span x 4 ms
ev = h["opst_cam_events_xytp"][s:e] # only this slice leaves disk
Episode order is not source order (conversion shards round-robin), so meta/camera.jsonl is the
only way to pair an episode with its raw stream.
With these you can build other representations -- event counts, time surfaces, voxel grids, other accumulation windows -- without re-running v2e, which is the most expensive stage of the pipeline (5.8 h for 1016 episodes).
Training only? Skip this folder. Opening
LeRobotDataset(repo_id)without anepisodesargument callssnapshot_downloadwith no allow-list, so all 20 GB come along:from huggingface_hub import snapshot_download snapshot_download(repo_id, repo_type="dataset", local_dir=root, ignore_patterns="raw_events/") LeRobotDataset(repo_id, root=root)
In-domain eval scenes (eval_scenes/)
Reproducing the in-domain numbers needs the scenes restored exactly as they were generated, which
run_ckpt_eval*.sh does through --replay_h5_dir on the source HDF5s. Those are 37 GB (25 Hz) /
267 GB (250 Hz) and are not published.
They do not have to be. eval_server.load_h5_episode reads only the root attrs (env_config,
seed, instruction, init_*) and the action dataset -- the camera frames, which are 99.99%
of the file, are re-rendered by the server. Stripped to that, a 250 Hz 20-scene set goes from
5.1 GB to 0.37 MB, and ships here under eval_scenes/. Restoring from the stripped files was
verified to reproduce the original scene exactly: object pose and velocity, bowl pose, arm pose
and action length all match.
huggingface-cli download mickeykang/dvla-can-250hz-events-250fps-delta-trim --repo-type dataset --include 'eval_scenes/*' --local-dir .
REPLAY_DIR=$PWD/eval_scenes/indomain_scenes_s20_250 ... bash overnight_delta_n3/run_policy_eval.sh
Out-of-distribution scenes ship here too, under eval_scenes/ood_scenes_s20_250. They are normally sampled
from seeds 9000000-9000019 at run time, but that only reproduces on the machine that produced the
published numbers: the sampler's bowl-clearance test reads mesh radii out of assets/, and the
result also depends on the robosuite/MuJoCo build and the floating-point path. Replaying the frozen
scenes removes all of that. The dumped scene names match the ones in the original eval logs, and
replaying reproduces the seed-sampled scene exactly (object, bowl and joints identical; end-effector
differs by 3e-08 because init_* is stored float32, a property of the original pipeline).
REPLAY_DIR=$PWD/eval_scenes/ood_scenes_s20_250 ... bash overnight_delta_n3/run_policy_eval.sh
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]
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