| --- |
| 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<n<1M |
| configs: |
| - config_name: motherboard |
| data_files: |
| - split: train |
| path: data/motherboard/meta/**/*.parquet |
| - config_name: pushT |
| data_files: |
| - split: train |
| path: data/pushT/meta/**/*.parquet |
| - config_name: all |
| default: true |
| data_files: |
| - split: train |
| path: data/**/meta/**/*.parquet |
| --- |
| |
| # React — Multi-Task Tactile-Visual Manipulation |
|
|
| Dense, contact-rich, synchronized multimodal interaction data collected from **human hands holding handheld GelSight tactile sensors** (no robot arm). Intended for **tactile-visual dynamics / world-model learning**. |
|
|
| > **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/<task>/ |
| ├── 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/<date>/episode_NNN/ |
| │ ├── view_left.mp4 view_middle.mp4 view_right.mp4 # 640×480 RGB |
| │ └── tactile_left.mp4 tactile_right.mp4 # 640×480 GelSight |
| ├── meta/<date>/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/<date>/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/<task>/depth/`) |
| Per-camera depth is shipped as **lossless FFV1 16-bit video** (`gray16le`): |
| ``` |
| data/<task>/depth/<date>/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/<task>/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). |
| |
| ## ⚠️ Known issue: tactile acquisition latency (~15 frames) |
| |
| Recordings **up to and including 2026-06-18** have 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 |
| **correctable**. The reference loader compensates at load time: |
| |
| ```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/<task>/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/). |
| |
