README.md

---
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.

![Tactile intensity timeline](figures/contact_intensity_full.png)

> **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`](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. |

![Comparison with other manipulation datasets](figures/dataset_figures/F7_comparison_table.png)

## 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`](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 recording 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
```

## Example dataloader — short contact-rich windows

A reference PyTorch `Dataset` is shipped under [`examples/react_window_dataset.py`](examples/react_window_dataset.py). It scans the processed `.pt` files, applies the contact filter, drops windows that overlap [`bad_frames.json`](bad_frames.json), and respects the per-date `active_sensors` field from [`tasks.json`](tasks.json).

```python
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:

![dataloader sample grid](figures/dataloader_examples/sample_grid.png)

One window played frame-by-frame with the sensor-frame overlay:

![dataloader sample GIF](figures/dataloader_examples/sample_window.gif)

Full demo script: [`examples/demo_react_window.py`](examples/demo_react_window.py).

## Recording-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 — 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`](docs/recording.md) | Hardware setup, camera serials, sensor + mocap layout, robot-free collection method |
| [`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).