Add dataset card with schema, quick start, caveats, and roadmap
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README.md
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---
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license: cc-by-4.0
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task_categories:
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- robotics
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tags:
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- robotics
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- tactile
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- manipulation
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- multimodal
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- gelsight
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- realsense
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- motion-capture
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pretty_name: React (Tactile-Visual Manipulation)
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size_categories:
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- 10K<n<100K
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configs:
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- config_name: mode1_v1
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data_files:
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- split: train
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path: processed/mode1_v1/**/episode_*.pt
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---
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# React
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Multimodal manipulation recordings from a bimanual setup with vision-based tactile sensors and motion capture.
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## Recording setup
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| Stream | Hardware | Native shape | Rate |
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|---|---|---|---|
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| 3× RealSense color | Intel D415 (serials `143322063538`, `104122062574`, `217222066989`) | 480×640×3 uint8 (BGR) | 30 FPS |
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| 3× RealSense depth | same | 480×640 uint16 (mm) | 30 FPS |
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| 2× GelSight tactile | GelSight Mini (left / right) | 480×640×3 uint8 | ~25 FPS, resampled to camera ticks |
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| 3× OptiTrack rigid bodies | `motherboard`, `sensor_left`, `sensor_right` | 7-vector (x, y, z, qx, qy, qz, qw) | ~120 Hz |
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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`.
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## Tasks recorded
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- **`motherboard`** — bimanual insertion / placement on a computer motherboard.
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## Repository layout
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```
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processed/
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└─ mode1_v1/
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├─ 2026-03-23/ # 3 episodes (2,141 / 1,948 / 2,109 frames)
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├─ 2026-05-10/ # 12 episodes ( 763 – 10,476 frames each)
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└─ 2026-05-11/ # 17 episodes ( 263 – 33,739 frames each)
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├─ episode_000.pt
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├─ episode_000.contact.json
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├─ episode_002.pt # episode_001 missing (corrupt at recording time)
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└─ ...
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```
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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.
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## `processed/mode1_v1/` schema
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Each `episode_*.pt` is a Python dict loadable with `torch.load(..., weights_only=False)`.
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| Key | Shape | dtype | Description |
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|---|---|---|---|
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| `view` | `(T, 3, 128, 128)` | uint8 | Overhead camera (`realsense/cam0/color`), center-cropped to square then bilinear-resized to 128×128 |
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| `tactile_left` | `(T, 3, 128, 128)` | uint8 | Left GelSight, same crop/resize |
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| `tactile_right` | `(T, 3, 128, 128)` | uint8 | Right GelSight, same crop/resize |
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| `timestamps` | `(T,)` | float64 | Camera timestamps (seconds, monotonic clock) |
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| `sensor_left_pose` | `(T, 7)` | float32 | Left GelSight rigid body OptiTrack pose, nearest-neighbor aligned to camera timestamps |
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| `sensor_right_pose` | `(T, 7)` | float32 | Right GelSight rigid body OptiTrack pose, same alignment |
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| `tactile_{left,right}_intensity` | `(T,)` | float32 | Per-frame mean per-pixel L2 distance from a contact-free reference frame |
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| `tactile_{left,right}_area` | `(T,)` | float32 | Per-frame fraction of pixels with L2 diff > `tau` |
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| `tactile_{left,right}_mixed` | `(T,)` | float32 | Mean of (diff × mask), captures intensity restricted to contact pixels |
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| `_contact_meta` | dict | — | Per-episode contact metadata: `tau`, drift between first/p01 reference frames, p01 reference indices, the chosen reference RGB frames, etc. |
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Each `.contact.json` is a small summary of the metric distributions plus drift diagnostics, intended for filtering / sanity checking without loading the full tensors.
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### Contact metric definition
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For each tactile sensor independently:
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1. Pick a contact-free **reference frame**: the ~0.1th-percentile-quietest frame by mean L2 distance to the temporal median (`reference_strategy = "p01"`).
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2. For each frame `t`, compute per-pixel `diff[t, x, y] = || frame[t, :, x, y] − ref[:, x, y] ||_2` (RGB L2 over channels).
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3. Then:
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- `intensity[t] = mean(diff[t])`
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- `area[t] = mean(diff[t] > tau)` (default `tau = 8.0` on the uint8 scale)
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- `mixed[t] = mean(diff[t] * (diff[t] > tau))`
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`_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.
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## Quick start
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```python
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import torch
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from huggingface_hub import hf_hub_download
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path = hf_hub_download(
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repo_id="yxma/React",
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repo_type="dataset",
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filename="processed/mode1_v1/2026-05-11/episode_000.pt",
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)
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ep = torch.load(path, weights_only=False)
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print(ep["view"].shape, ep["tactile_left_intensity"].shape)
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# torch.Size([263, 3, 128, 128]) torch.Size([263])
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```
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To load contact metadata only (much smaller — useful for filtering):
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```python
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import json
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path = hf_hub_download(
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repo_id="yxma/React",
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repo_type="dataset",
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filename="processed/mode1_v1/2026-05-11/episode_000.contact.json",
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)
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meta = json.load(open(path))
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print(meta["drift_left"], meta["drift_right"], meta["drift_warning"])
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```
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## Known caveats
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- **Missing episode**: `2026-05-11/episode_001` was lost at recording time (HDF5 superblock never finalized when the writer was killed mid-write). It is intentionally absent.
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- **Lossy resize**: the 128×128 `view` and tactile fields are downsampled from native 480×640. Native resolution is **not** preserved in this release.
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- **Single camera**: only `realsense/cam0/color` is included. The other two RealSense views and all depth streams are not in `processed/mode1_v1/`.
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- **OptiTrack alignment**: the per-step poses are nearest-neighbor matched to camera ticks. The full ~120 Hz pose streams are not preserved here.
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- **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.
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## Roadmap
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A **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.
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## Citation
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If you use this dataset, please cite (TODO: add bibtex).
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