--- license: cc-by-4.0 task_categories: - robotics tags: - robotics - tactile - manipulation - multimodal - gelsight - realsense - motion-capture - dynamics - world-model pretty_name: React (Tactile-Visual Manipulation) size_categories: - 100K **138 min of synchronized multimodal interaction · 88 min (66 %) of confirmed bimanual tactile contact · 4,136 distinct contact events · 240 k frames @ 30 Hz across 3 × RGB-D + 2 × GelSight + 3-body OptiTrack** ## At a glance | | | |---|---| | Intended use | Dynamics / world-model learning over short multimodal windows. Sample short trajectories (1 s – 10 s); episode boundaries are not action boundaries. | | Total synchronized duration | **138.4 min** at 30 Hz (239,759 multimodal frames) | | Bimanual tactile-contact time | **87.9 min — 66 % of frames** (4,136 contact events, median 0.73 s) | | Cameras | 3× Intel RealSense D415 (color + depth), 480×640, 30 FPS | | Tactile | 2× GelSight Mini (left, right) | | Motion capture | OptiTrack VRPN, 3 rigid bodies, ~120 Hz | | Tasks | `motherboard` (more coming) | | License | CC-BY-4.0 | ![Comparison with other manipulation datasets](figures/dataset_figures/F7_comparison_table.png) ## Recording sessions | Date | Kind | Active sensors | Notes | |---|---|---|---| | 2026-03-23 | pilot | **right only** | Early trials. The left GelSight and left rigid body were not in use; ignore `tactile_left*` and `sensor_left_pose` for these recordings. | | 2026-05-10 | session | left + right | First full bimanual session. | | 2026-05-11 | session | left + right | Largest session. A handful of GelSight LED-flicker frames + one mocap teleport; see [`bad_frames.json`](bad_frames.json). | See [`tasks.json`](tasks.json) for the machine-readable registry (per-date `active_sensors`, etc.). ## Quick start ```python # Load by task with `datasets` from datasets import load_dataset ds = load_dataset("yxma/React", "motherboard", split="train") ``` Or grab a single episode file directly: ```python import torch from huggingface_hub import hf_hub_download path = hf_hub_download( repo_id="yxma/React", repo_type="dataset", filename="processed/mode1_v1/motherboard/2026-05-11/episode_003.pt", ) ep = torch.load(path, weights_only=False) # ep["view"] (T, 3, 128, 128) uint8 — overhead cam # ep["tactile_left"], ep["tactile_right"] (T, 3, 128, 128) uint8 # ep["sensor_left_pose"], ep["sensor_right_pose"] # (T, 7) float32 — xyz + quaternion # ep["timestamps"] (T,) float64 # Plus per-frame contact metrics: tactile_{side}_{intensity, area, mixed} ``` Sampling short windows for dynamics learning: ```python import json with open("bad_frames.json") as f: bad = json.load(f)["episodes"] # Drop ~0.085 % of frames flagged in bad_frames.json — see docs/quality.md # For 2026-03-23 episodes, also ignore the left-sensor fields (right-only pilot). ``` ## Episode-file previews Per-file GIF previews live under [`figures/episode_previews/`](figures/episode_previews) — first 2 minutes at 10× speed, showing all 3 RealSense cameras with projected GelSight axes plus both tactile pads. (The on-disk recording unit is called an "episode" purely for file naming — episode boundaries don't carry semantic meaning for this dataset.) ## Repository layout ``` README.md # this file tasks.json # task / session registry bad_frames.json # data-quality skip-list processed/mode1_v1///episode_*.pt # per-file tensors figures/ # previews + analysis figures docs/ # extended documentation ``` ## More documentation | File | Contents | |---|---| | [`docs/recording.md`](docs/recording.md) | Hardware setup, camera serials, sensor + mocap layout | | [`docs/schema.md`](docs/schema.md) | Full `.pt` field reference and contact-metric definitions | | [`docs/quality.md`](docs/quality.md) | Data-quality breakdown (per-mode), `bad_frames.json` schema, dataloader recipe, inspection figures | | [`docs/figures.md`](docs/figures.md) | Dataset statistics + analysis gallery (F1–F8) | | [`docs/caveats.md`](docs/caveats.md) | Known caveats and roadmap | ## License Released under [Creative Commons Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) (CC-BY-4.0). ## Citation If you use this dataset, please cite (TODO: add bibtex).