license: cc-by-4.0
task_categories:
- robotics
tags:
- robotics
- tactile
- manipulation
- multimodal
- gelsight
- realsense
- motion-capture
- dynamics
- world-model
- human-collected
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 data collected from human hands holding handheld GelSight tactile sensors — no robot arm involved. Intended for tactile-visual dynamics / world-model learning, not a policy / demonstration dataset.
126 min of robot-free human-hand multimodal interaction · 81 min (66 %) of confirmed bimanual tactile contact · 221,621 frames @ 30 Hz across 3 × RGB-D + 2 × GelSight + 3-body OptiTrack
What's different about this dataset
| Robot-arm-free | Recorded directly from a human operator holding two GelSight Mini sensors. No robot kinematics, no embodiment bias, no robot occluding the scene. |
| Tactile + RGB-D + mocap, simultaneous | Most manipulation datasets ship one of these. React ships all three, synchronized to a common 30 Hz clock. |
| Contact-dense | 66 % of all frames have confirmed tactile contact on at least one sensor — see figures/contact_intensity_full.png. |
| Long, continuous interaction | Recordings are minutes long, not seconds. Median recording duration is 4 min; longest 19 min. Good for short-window sampling of dynamics, not for action-conditioned policy learning. |
At a glance
| Embodiment | Human hands (no robot) — handheld GelSight sensors with motion-capture rigid bodies |
| Intended use | Dynamics / world-model learning over short multimodal windows. Sample short trajectories (1 s – 10 s); recording-file boundaries are not action boundaries. |
| Total synchronized duration | 126.0 min at 30 Hz (221,621 multimodal frames) |
| Bimanual tactile-contact time | 81.4 min — 66 % of frames (median event duration 0.73 s) |
| Cameras | 3× Intel RealSense D415 (color + depth), 480×640, 30 FPS |
| Tactile | 2× GelSight Mini (left, right), handheld |
| 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 |
|---|---|---|---|
| 2026-05-10 | 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 recording 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
Example dataloader — short contact-rich windows
A reference PyTorch Dataset is shipped under examples/react_window_dataset.py. It scans the processed .pt files, applies the contact filter, drops windows that overlap bad_frames.json, and respects the per-date active_sensors field from tasks.json.
from examples.react_window_dataset import ReactWindowDataset
from torch.utils.data import DataLoader
ds = ReactWindowDataset(
data_root="processed/mode1_v1/motherboard",
bad_frames_path="bad_frames.json",
tasks_json_path="tasks.json",
window_length=16, # frames per window
stride=1, # within-window stride (1 = consecutive)
window_step=16, # step between window starts (overlap control)
contact_metric="mixed", # "intensity" | "area" | "mixed"
tactile_threshold=0.4,
min_contact_fraction=0.6, # ≥ 60 % of window frames must have contact
which_sensors="any", # "any" | "both" | "left" | "right"
skip_bad_frames=True,
respect_active_sensors=True,
)
print(len(ds), "windows")
loader = DataLoader(ds, batch_size=8, shuffle=True, num_workers=2)
With the defaults shown above, the dataset assembles ~9.2 k contact-rich 16-frame windows across the 27 recordings. Each sample is a dict of (T, …) tensors plus metadata (episode, frame_start, active_sensors, …).
Example output
Four random windows, time runs left→right; each cell is view | tactile_left | tactile_right with sensor frame axes (X red, Y green, Z blue-ish) projected onto the view:
One window played frame-by-frame with the sensor-frame overlay:
Full demo script: examples/demo_react_window.py.
Recording-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 — these boundaries don't carry semantic / action 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, robot-free collection method |
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).