--- 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 **173 min · 312 k frames @ 30 Hz · 3× RGB + 2× GelSight + 2× wrist + OptiTrack · 3 tasks** ![dataset statistics](assets/stats_wrist_era.png) *Per-task scale, contact-force distribution, bimanual contact occupancy, and tactile validity for the wrist-camera era. Panel B shows the ECDF of unsaturated contact force; the steps are real — the force calibration ends in an isotonic (piecewise-constant) stage, so its output is quantised. See [Known limits](#known-limits).* ## What is published Episodes are **cut into clean segments** before release: every span a defect detector flagged is removed, and each remaining span of at least 30 s is published as its own episode. So the flagged frames are the **gaps between** published episodes, and you do not need to consult `bad_frames.json` before training on `data//`. | Task | Source recordings | Published segments | Duration | Median contact force | |---|---|---|---|---| | **motherboard** | 12 | 25 | 93.9 min | 2.94 N | | **pushT** | 10 | 31 | 32.5 min | 4.29 N | | **rope** | 10 | 27 | 47.1 min | 1.38 N | | *(total)* | *32* | *83* | *173.5 min* | | `data/validation/` holds a separate 5-segment / 7.1 min set from an earlier session (2026-09-09) that carries **different wrist cameras and a different calibration epoch**. It is not a random held-out split — training on `data/` and evaluating there measures domain shift. See its own README. ![earlier session](assets/stats_arducam_session.png) ## Known limits Read these before using the force channel or the tactile stream. **Force saturates at 7.87 N.** The calibration was fitted on presses up to 8 N, and its final stage is an isotonic regression, which cannot extrapolate — anything above the fitted range is clipped to 7.87 N. Affected fraction of **contact** frames: - `motherboard` — 8.3 % - `pushT` — 8.4 % - `rope` — 0.6 % Treat those frames as **right-censored**, not as measurements. The force labels also carry roughly **1 N of held-out error**, so do not compare models at 0.1 N resolution. **Tactile frames repeat.** The GelSight Mini tops out near 17.8 Hz while rows are written at 29.8 Hz, so only about **56–58 %** of rows are a fresh sensor reading. `tactile_{left,right}_is_new` marks which. Train tactile dynamics on the flagged rows, not on all of them. **`duration_s` is `n_frames / 30`**, the nominal write tick. The RealSense streams actually run at 29.80 Hz. ## Format — LeRobot-style video release Each episode ships as **7 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 (data/ ≈ 21 GB vs ~1.9 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 │ └── wrist_left.mp4 wrist_right.mp4 # 640×480 wrist cams ├── 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, 2=rope) | | `force_{L,R}_normal_n` | float32 | estimated normal contact force (N) — **censored at 7.87 N**, see Known limits | | `force_{L,R}_penetration_mm` | float32 | gel penetration depth (mm) | | `force_{L,R}_source_frame` | int | the raw tactile frame this force was computed from | | `tactile_{L,R}_is_new` | bool | True when the row is a fresh sensor reading, not a repeat | | `timestamp` | float64 | camera clock (s) | | `sensor_left_pose`, `sensor_right_pose` | list[7] | OptiTrack world pose of each GelSight (xyz metres + quat **xyzw**, scalar-LAST — `scipy...Rotation.from_quat` takes it as-is) | | `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 and calibration | Task | Dates | Source recordings | Published segments | Duration | Calibration epoch | |---|---|---|---|---|---| | **motherboard** | 2026-09-11, 2026-09-12 | 12 | 25 | 93.9 min | 2026-09-09 | | **pushT** | 2026-09-10, 2026-09-11 | 10 | 31 | 32.5 min | 2026-09-09 | | **rope** | 2026-09-11 | 10 | 27 | 47.1 min | 2026-09-09 | | **validation** | 2026-09-09 | 5 | 9 | 25.2 min | 2026-09-09 | All currently published sessions share the **2026-09-09** extrinsics. Camera extrinsics are used only for the projection overlay; **stored poses are OptiTrack world-frame** and independent of calibration. Poses are **Z-up**. `data/validation/` mixes two tasks and two sessions of the same date, and its episode numbers are **not** the recorder's — the recorder reuses numbers within a date, so every row there carries `source_recording`. Read its README before using it. See [`tasks.json`](tasks.json) for the machine-readable registry. ## Downloading — depth is optional The dataset splits into a **lightweight core** (RGB + tactile + wrist + poses, ~21 GB) and an **optional depth tree** (`data/validation/depth/`, ~6 GB lossless; only the 2026-09-09 session has depth). Depth lives in its own subtree so you can skip it entirely. ```python from huggingface_hub import snapshot_download # Core only — RGB + tactile + wrist + parquet, NO depth (~21 GB) snapshot_download("yxma/React", repo_type="dataset", ignore_patterns=["*/depth/*"]) # Everything including depth (~27 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). ## World frame — Z-up as published, Y-up on request Every pose in this release is **Z-up**: `sensor_{left,right}_pose`, `object_pose`, `force_{left,right}_target_pose`, and the `T_mocap_to_cam` extrinsics in each `calibration/`. Each subset says so itself, in `episodes.jsonl` (`"up_axis": "z"`) and in every calibration file, so nothing has to infer it from a directory name. OptiTrack records Y-up. The release is rotated once, by `R_x(-90)`: `(x, y, z) -> (x, -z, y)`. If you want the raw convention, ask the loader: ```python ds = ReactVideoDataset("data/motherboard", up_axis="y") # default is "z" cal = ds.calibration() # comes back in the SAME convention ``` **Take the calibration from the dataset, not from disk.** This is a rotation of the world frame, so poses and `T_mocap_to_cam` have to move together — one without the other leaves every projection up to 165 px from the sensor and raises nothing. `react_toolbox.frames` is the same conversion if you need it directly: `convert_poses(poses7, to_zup=)`, `as_up_axis(cal, want)`. The invariant worth knowing: **rotating both changes no picture.** Measured on `data/validation` over 450 (frame, camera, sensor) combinations, the projected GelSight centre moves 0.000000 px between the two conventions. Newtons, timestamps and every video frame are identical either way. ## Tactile acquisition latency — does NOT affect any published episode Recordings up to and including **2026-06-18** had a GelSight-vs-camera capture lag of ≈15 frames (~0.5 s), caused by a recording-side `cv2.VideoCapture` V4L2 buffer that was never flushed. It was fixed in the rig on **2026-06-27**. **Every episode currently published was recorded on 2026-09-09 or later**, and each one carries per-sensor GelSight timestamps, so the tactile stream is resampled onto the camera clock during the build. Verified across all 80 published force records: `tactile_timestamped` is True for every one, and `tactile_align.gel_lag_frames` returns **0**. **Do not apply a 15-frame shift.** Doing so would introduce the half-second misalignment this note used to warn about. The correction is relevant only if you obtain one of the withdrawn 2026-05/06 releases. ## 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/).