| --- |
| 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. |
|
|
| ### 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`. |
|
|
| ```python |
| 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 an `episodes` |
| > argument calls `snapshot_download` with no allow-list, so all 20 GB come along: |
| > ```python |
| > 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. |
|
|
| ```bash |
| huggingface-cli download mickeykang/dvla-can-250hz-events-250fps-delta-trim --repo-type dataset --include 'eval_scenes/*' --local-dir . |
| REPLAY_DIR=$PWD/eval_scenes/indomain_scenes_s20_250 ... bash overnight_delta_n3/run_policy_eval.sh |
| ``` |
|
|
| Out-of-distribution scenes ship here too, under `eval_scenes/ood_scenes_s20_250`. They are normally sampled |
| from seeds 9000000-9000019 at run time, but that only reproduces on the machine that produced the |
| published numbers: the sampler's bowl-clearance test reads mesh radii out of `assets/`, and the |
| result also depends on the robosuite/MuJoCo build and the floating-point path. Replaying the frozen |
| scenes removes all of that. The dumped scene names match the ones in the original eval logs, and |
| replaying reproduces the seed-sampled scene exactly (object, bowl and joints identical; end-effector |
| differs by 3e-08 because `init_*` is stored float32, a property of the original pipeline). |
|
|
| ```bash |
| REPLAY_DIR=$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 |
|
|
| [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] |
| ``` |
