--- license: cc-by-4.0 task_categories: - robotics tags: - robotics - tactile - manipulation - multimodal - gelsight - realsense - motion-capture - world-model - human-collected - lerobot pretty_name: React (Tactile-Visual Manipulation) size_categories: - 100K **133 min · 240 k frames @ 30 Hz · 3× RGB + 2× GelSight + OptiTrack · 2 tasks** ## Format — LeRobot-style video release Each episode ships as **5 MP4 video streams** (640×480, H.264) + a **per-frame parquet** of poses and contact metrics. This matches how LeRobot / DROID / Open X-Embodiment ship manipulation data: tiny on disk (whole dataset ≈ 4.3 GB vs ~1 TB raw), random-access decodable, training-ready. ``` data// ├── calibration/ # OptiTrack→camera extrinsics for this task │ ├── T_mocap_to_cam_{left,middle,right}.json │ ├── T_gel_to_rigid_{left,right}.json │ └── calibration.json # epoch, applies-to dates, RMSE, chain ├── videos//episode_NNN/ │ ├── view_left.mp4 view_middle.mp4 view_right.mp4 # 640×480 RGB │ └── tactile_left.mp4 tactile_right.mp4 # 640×480 GelSight ├── meta//episode_NNN.parquet # one row per frame (see below) ├── episodes.jsonl # one row per episode ├── segments.json # clean-segment index (no bad frames) ├── bad_frames.json # quality intervals per episode └── previews//episode_NNN.mp4 # 1280×480 viewer-layout preview ``` ### parquet columns (per frame, aligned to video frame `i`) | Column | Type | Meaning | |---|---|---| | `frame_idx` / `frame_index` | int | 0…T-1, matches MP4 frame index | | `episode` / `episode_index` | str / int | source episode key and its 0-based index within the task | | `task` / `task_index` | str / int | task name and index (0=motherboard, 1=pushT) | | `timestamp` | float64 | camera clock (s) | | `sensor_left_pose`, `sensor_right_pose` | list[7] | OptiTrack world pose of each GelSight (xyz + quat wxyz) | | `object_pose` | list[7] | OptiTrack world pose of the manipulated object (NaN where the object body was not tracked — e.g. all pushT) | | `tactile_{L,R}_{intensity,area,mixed}` | float32 | contact metrics (computed at full 640×480) | | `source_h5_frame` | int | index into the original recording | **Decoded frames are RGB** (standard decoder convention) for all five RGB streams. ### depth (optional, `data//depth/`) Per-camera depth is shipped as **lossless FFV1 16-bit video** (`gray16le`): ``` data//depth//episode_NNN/depth_{left,middle,right}.mkv ``` - uint16, **millimeters**; `0` = no return / invalid. - Frame `i` aligns to the RGB video frame `i` and parquet row `i`. - Decode with PyAV (`frame.to_ndarray()` → `(480, 640)` uint16). cv2 cannot read 16-bit video. - Load via `ReactVideoDataset(..., load_depth=True)`. ## Tasks | Task | Episodes | Dates | Duration | Clean segments | Calibration | |---|---|---|---|---|---| | **motherboard** | 32 | 2026-05-10/11/19 | 108 min | 76 (107 min) | **May-12** (RMSE ~5 mm) | | **pushT** | 4 | 2026-06-18 | 25 min | 17 (25 min) | **June-26** (RMSE ~0.6 px) | See [`tasks.json`](tasks.json) for the machine-readable registry (per-task dates, sensors, calibration epoch, world-frame offsets). ### Calibration epochs Cameras were **recalibrated between tasks**. Each task points to the calibration valid for its recordings: - `motherboard` → **May-12** extrinsics (`data/motherboard/calibration/`) - `pushT` → **June-26** extrinsics (`data/pushT/calibration/`) Camera extrinsics are used only for the projection overlay; **stored poses are OptiTrack world-frame** and independent of calibration. The 2026-05-19 motherboard session had a redefined world origin; an offset `(0.23, 0, 0.175) m` is already baked into its poses so all dates share one frame (recorded in `episodes.jsonl`). ## Downloading — depth is optional The dataset splits into a **lightweight core** (RGB + tactile + poses, ~4.4 GB) and an **optional depth tree** (`data//depth/`, ~33 GB lossless). Depth lives in its own subtree so you can skip it entirely. ```python from huggingface_hub import snapshot_download # Core only — RGB + tactile + parquet, NO depth (~4.4 GB) snapshot_download("yxma/React", repo_type="dataset", ignore_patterns=["*/depth/*"]) # Everything including depth (~37 GB) snapshot_download("yxma/React", repo_type="dataset") # One task only snapshot_download("yxma/React", repo_type="dataset", allow_patterns=["data/motherboard/*"], ignore_patterns=["*/depth/*"]) ``` Or use the helper: `python examples/download.py --no-depth` (see [`examples/download.py`](examples/download.py)). The `ReactVideoDataset` loader **never touches depth unless you pass `load_depth=True`**, so depth-free training requires no depth download. ## Loading ```python from examples.react_video_dataset import ReactVideoDataset ds = ReactVideoDataset("data/motherboard", window_length=16, mode="segment") sample = ds[0] # sample["view_middle"]: (16, 480, 640, 3) uint8 RGB # sample["tactile_left"]: (16, 480, 640, 3) uint8 RGB # sample["sensor_left_pose"]: (16, 7) float32 ``` `mode="segment"` iterates clean spans (no bad frames by construction); `mode="window"` slides over whole episodes and skips `bad_frames.json` intervals. Backend: PyAV (install `decord` for faster random access). ## ✅ Tactile latency corrected (was ~15 frames) Recordings **up to and including 2026-06-18** HAD a GelSight-vs-camera capture lag of **≈15 frames (~0.5 s)**: the tactile stream at index `i` was physically captured ~15 frames *before* the camera/pose at the same index. Cause: a recording-side `cv2.VideoCapture` V4L2 buffer that was never flushed (throttled reads + no `BUFFERSIZE=1` + default pixel format). Fixed in the rig on 2026-06-27; **future recordings will not have this lag**. The streams are stored frame-aligned by tick index, so this lag is baked in but **now corrected in the published data** (tactile shifted +15f, rebuilt from raw H5). No loader flag needed. The loader still accepts `tactile_latency=` for raw data: ```python ds = ReactVideoDataset("data/motherboard", tactile_latency=15) # pairs view[i] with tactile[i+15] ``` `tactile_latency` shifts both the tactile videos and the tactile contact-scalar columns; poses/views/depth are unchanged. Set `tactile_latency=0` for the raw (uncompensated) data. The exact per-session value should be re-measured with `camera_stream/measure_gelsight_latency.py`. ## Data quality Per-task `bad_frames.json` flags `intensity_spikes`, `pose_teleports_{L,R}`, `ot_loss_{L,R}` (OptiTrack track loss). Overall flagged: motherboard 0.90 %, pushT 0.67 %. `segments.json` already excludes them. ## Notes - **Depth** is available in the source recordings and will be added under `data//depth/` in a later upload. - One pushT source recording (`episode_004`) was corrupt and excluded. - The previous single-task `.pt` release (`episodes/`, `segments/`) is superseded by this video format. ## License [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).