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<n<1M
configs:
- config_name: motherboard
data_files:
- split: train
path: processed/mode1_v1/motherboard/**/episode_*.pt
- config_name: all
data_files:
- split: train
path: processed/mode1_v1/**/episode_*.pt
React
Dense, contact-rich, synchronized multimodal interaction recordings for tactile-visual dynamics / world-model learning — not a policy / demonstration dataset.
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 |
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. |
See tasks.json for the machine-readable registry (per-date active_sensors, etc.).
Quick start
# 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:
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:
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/ — 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/<task>/<date>/episode_*.pt # per-file tensors
figures/ # previews + analysis figures
docs/ # extended documentation
More documentation
| File | Contents |
|---|---|
docs/recording.md |
Hardware setup, camera serials, sensor + mocap layout |
docs/schema.md |
Full .pt field reference and contact-metric definitions |
docs/quality.md |
Data-quality breakdown (per-mode), bad_frames.json schema, dataloader recipe, inspection figures |
docs/figures.md |
Dataset statistics + analysis gallery (F1–F8) |
docs/caveats.md |
Known caveats and roadmap |
License
Released under Creative Commons Attribution 4.0 (CC-BY-4.0).
Citation
If you use this dataset, please cite (TODO: add bibtex).