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React

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React

Multimodal manipulation recordings from a bimanual setup with vision-based tactile sensors and motion capture.

Recording setup

Stream	Hardware	Native shape	Rate
3× RealSense color	Intel D415 (serials `143322063538`, `104122062574`, `217222066989`)	480×640×3 uint8 (BGR)	30 FPS
3× RealSense depth	same	480×640 uint16 (mm)	30 FPS
2× GelSight tactile	GelSight Mini (left / right)	480×640×3 uint8	~25 FPS, resampled to camera ticks
3× OptiTrack rigid bodies	`motherboard`, `sensor_left`, `sensor_right`	7-vector (x, y, z, qx, qy, qz, qw)	~120 Hz

All streams share a common monotonic timestamp axis recorded under timestamps. Per-tracker OptiTrack streams carry their own higher-rate timestamps under optitrack/<body>/timestamps.

Tasks

The dataset is organized task-first so new tasks can be added without renaming or recompute. Current tasks:

Task	Description	Dates	Episodes
`motherboard`	Bimanual manipulation of components on a computer motherboard	2026-03-23, 2026-05-10, 2026-05-11	30

tasks.json at the repo root is the source of truth for the task registry.

Repository layout

tasks.json                                          # registry
processed/
└─ mode1_v1/
    └─ <task>/
        └─ <date>/
            ├─ episode_000.pt
            ├─ episode_000.contact.json
            └─ ...

The processed/mode1_v1/ view is a task-specific slice of the underlying raw recordings, not the full sensor suite. It was produced by twm/preprocess.py + twm/contact_index.py from a private raw HDF5 mirror.

`processed/mode1_v1/` schema

Each episode_*.pt is a Python dict loadable with torch.load(..., weights_only=False).

Key	Shape	dtype	Description
`view`	`(T, 3, 128, 128)`	uint8	Overhead camera (`realsense/cam0/color`), center-cropped to square then bilinear-resized to 128×128
`tactile_left`	`(T, 3, 128, 128)`	uint8	Left GelSight, same crop/resize
`tactile_right`	`(T, 3, 128, 128)`	uint8	Right GelSight, same crop/resize
`timestamps`	`(T,)`	float64	Camera timestamps (seconds, monotonic clock)
`sensor_left_pose`	`(T, 7)`	float32	Left GelSight rigid body OptiTrack pose, nearest-neighbor aligned to camera timestamps
`sensor_right_pose`	`(T, 7)`	float32	Right GelSight rigid body OptiTrack pose, same alignment
`tactile_{left,right}_intensity`	`(T,)`	float32	Per-frame mean per-pixel L2 distance from a contact-free reference frame
`tactile_{left,right}_area`	`(T,)`	float32	Per-frame fraction of pixels with L2 diff > `tau`
`tactile_{left,right}_mixed`	`(T,)`	float32	Mean of (diff × mask), captures intensity restricted to contact pixels
`_contact_meta`	dict	—	Per-episode contact metadata: `tau`, drift between first/p01 reference frames, p01 reference indices, the chosen reference RGB frames, etc.

Each .contact.json is a small summary of the metric distributions plus drift diagnostics, intended for filtering / sanity checking without loading the full tensors.

Contact metric definition

For each tactile sensor independently:

Pick a contact-free reference frame: the ~0.1th-percentile-quietest frame by mean L2 distance to the temporal median (reference_strategy = "p01").
For each frame t, compute per-pixel diff[t, x, y] = || frame[t, :, x, y] − ref[:, x, y] ||_2 (RGB L2 over channels).
Then:
- intensity[t] = mean(diff[t])
- area[t] = mean(diff[t] > tau) (default tau = 8.0 on the uint8 scale)
- mixed[t] = mean(diff[t] * (diff[t] > tau))

_contact_meta["drift_warning"] is True if either sensor's drift (L2 distance between the first frame and the p01-reference frame) exceeds 2·tau; in this release no episode triggers it.

Quick start

Load one task via datasets:

from datasets import load_dataset
ds = load_dataset("yxma/React", "motherboard", split="train")
# Each row is one .pt file path; the actual tensors live inside.

Or load a single episode 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)
print(ep["view"].shape, ep["tactile_left_intensity"].shape)
# torch.Size([10032, 3, 128, 128]) torch.Size([10032])

Load contact metadata only (much smaller — useful for filtering):

import json
path = hf_hub_download(
    repo_id="yxma/React",
    repo_type="dataset",
    filename="processed/mode1_v1/motherboard/2026-05-11/episode_003.contact.json",
)
meta = json.load(open(path))
print(meta["drift_left"], meta["drift_right"], meta["drift_warning"])

Known caveats

Missing / dropped episodes on motherboard/2026-05-11:
- episode_000 and episode_002 — short test recordings (8.8s and 10.4s) with no tactile contact on either sensor; intentionally excluded.
- episode_001 — lost at recording time (HDF5 superblock never finalized when the writer was killed mid-write); intentionally absent.
- The remaining episode IDs are non-contiguous as a result. Don't infer ordering from filename gaps.
Lossy resize: the 128×128 view and tactile fields are downsampled from native 480×640. Native resolution is not preserved in this release.
Single camera: only realsense/cam0/color is included. The other two RealSense views and all depth streams are not in processed/mode1_v1/.
OptiTrack alignment: the per-step poses are nearest-neighbor matched to camera ticks. The full ~120 Hz pose streams are not preserved here.
Mode is opinionated: contact metrics depend on the chosen tau and the p01 reference strategy. If you want a different tau, re-deriving from raw is necessary.

Roadmap

More tasks — registry in tasks.json will grow.
LeRobot-format full-fidelity variant (lerobot/v1.0/) is planned. It will include all three RealSense color and depth streams, GelSight at native resolution (FFV1 lossless), full-rate OptiTrack pose tracks for all three rigid bodies, and HF-native browser previews. The current processed/mode1_v1/ slice will remain as a stable training-task view.