Datasets:

zekaiwang
/

trex_dataset

---
license: mit
language:
- en
task_categories:
- robotics
tags:
- LeRobot
- robotics
- manipulation
- tactile
- bimanual
- dexterous-manipulation
pretty_name: T-Rex Dataset
size_categories:
- 1M<n<10M
configs:
- config_name: frames
  data_files:
  - split: train
    path: data/chunk-*/file-*.parquet
  default: true
- config_name: episodes
  data_files:
  - split: train
    path: episodes_preview.parquet
---

# T-Rex Dataset

A large-scale, tactile-reactive bimanual manipulation dataset, collected via teleoperation on a
Dexmate Vega-1 robot with two Sharpa Wave dexterous hands. Stored as a
[LeRobotDataset v3.0](https://github.com/huggingface/lerobot).

[🌐 Project Page](https://tactile-rex.github.io/) · [✍️ Paper (arXiv)](https://arxiv.org/abs/2606.17055) · [💻 Code (T-Rex)](https://github.com/ZhuoyangLiu2005/T-Rex) · [🚀 Dataset Quickstart](https://github.com/ZhuoyangLiu2005/T-Rex/tree/main/dataset_quickstart) · [📓 Colab notebook](https://colab.research.google.com/github/ZhuoyangLiu2005/T-Rex/blob/main/dataset_quickstart/quickstart.ipynb)

<p align="center">
  <img src="assets/dataset_overview.png" width="100%">
  <br>
  <em>One episode from each of 20 motor primitives (head-camera view, cropped to the workspace), each with a different object.</em>
</p>

<p align="center">
  <img src="assets/hardware_setup.png" width="100%">
  <br>
  <em>Teleoperation setup: Manus gloves + VIVE trackers drive the bimanual Dexmate Vega-1 with two Sharpa Wave hands; observations come from a head-mounted ZED X Mini camera and two wide-view ZED X One S wrist cameras.</em>
</p>

## At a glance

| | |
|---|---|
| Robot | Dexmate Vega-1 dual-arm (7 actuated joints per arm) + 2× Sharpa Wave dexterous hands (5 fingertip tactile sensors each) |
| Modalities | 3 RGB cameras · 10 raw tactile + 10 deformation tactile videos · 6-axis fingertip wrenches · joint states/targets |
| Episodes | 5,464 |
| Frames | 5,473,459 (~50 hours @ 30 fps) |
| Tasks | 5,370 language-annotated trajectories · 22 motor primitives · 207 objects |
| Format | LeRobotDataset v3.0 (`codebase_version: v3.0`) |

## Composition

<p align="center">
  <img src="assets/dataset_analysis.png" width="100%">
  <br>
  <em>Object categories, episodes per motor primitive, and per-object episode counts.</em>
</p>

## Collection setup

The full teleoperation/hardware stack used to collect the dataset is open-sourced in
[`hardware_code/`](https://github.com/ZhuoyangLiu2005/T-Rex/tree/main/hardware_code) in the T-Rex repo.

- **Robot.** The Dexmate Vega-1 is a dual-arm mobile robot with 7 actuated joints per arm, here
  equipped with two Sharpa Wave dexterous hands. During collection the wheels, torso, and head
  joints are fixed; only the 14 arm joints and the two hands are actuated.
- **Cameras.** A head-mounted ZED X Mini stereo camera (its left monocular RGB stream is recorded)
  plus two wide-view ZED X One S monocular RGB cameras mounted on the wrists, posed so the head
  camera covers the full reachable workspace while the wrist cameras keep the fingers visible
  without significant palm occlusion. All three streams are recorded at 640×360.
- **Tactile.** Each hand carries five fingertip tactile sensors. Per sensor, the dataset stores the
  raw sensor image and the estimated deformation map (both as video), and the estimated 6-axis net
  wrench (`observation.tactile_force`).
- **Teleoperation.** Manus gloves capture fingertip positions relative to the hand base, retargeted
  to the Sharpa Wave hands with the manufacturer's differential-inverse-kinematics package
  (Pinocchio + CasADi). Two VIVE trackers provide SE(3) wrist poses, converted to arm joint
  commands via differential inverse kinematics ([Pink](https://github.com/stephane-caron/pink)),
  low-pass filtered, and tracked by the manufacturer's low-level cascade PID controller. A 30 Hz
  high-level thread records observations and joint-space targets while asynchronously updating a
  300 Hz low-level control thread — the dataset's 30 fps matches the high-level loop, and `action`
  holds its 30 Hz joint-space targets.

## Per-frame features

| feature | shape | description |
|---|---|---|
| `observation.state` | `(58,)` | joint **positions**, laid out `[L_arm 7 \| L_hand 22 \| R_arm 7 \| R_hand 22]` |
| `action` | `(58,)` | **target** joint positions (same layout) |
| `observation.tactile_force` | `(60,)` | per-fingertip 6-axis wrench: `(left, right) × (thumb…pinky) × (Fx, Fy, Fz, Mx, My, Mz)` |
| `observation.images.{head_left, left_wrist, right_wrist}` | `360×640×3` | scene + wrist RGB cameras |
| `observation.images.tactile_{left,right}_raw_{finger}` | `240×320` (grayscale) | raw tactile sensor images (10 = 2 hands × 5 fingers) |
| `observation.images.tactile_{left,right}_deform_{finger}` | `240×240` (grayscale) | tactile deformation fields (10) |

Finger order is `thumb, index, middle, ring, pinky`. Full per-dimension joint names are in
`meta/info.json` (`features[*].names`).

## Per-episode metadata

`meta/episodes/*.parquet` carries language and task labels per episode, in addition to the standard
LeRobot fields (`episode_index`, `tasks`, `length`, per-feature stats, video pointers):

| field | description |
|---|---|
| `caption` | human-verified natural-language instruction (5,370 unique) |
| `motor_primitive` | one of 22 primitives (`reach`, `lift_and_place`, …) |
| `object` | canonical object name (207 unique) |
| `target` | canonical target/receptacle (only set for `lift_and_place`; null otherwise) |

## Tactile video encoding (read before decoding)

The raw and deformation tactile videos are stored **losslessly** (`libx264 -qp 0`) because their
pixel values are physically meaningful (raw sensor images and deformation maps). They are grayscale —
the signal lives entirely in the **luma (Y) plane** — and use **full-range** `yuvj420p`, so values
span the full `0–255` with no range conversion. Decode the luma plane to recover the original `uint8`
images exactly, e.g. `frame.to_ndarray(format="gray")` in PyAV.

> **No web thumbnails for tactile.** Lossless H.264 forces the *High 4:4:4 Predictive* profile,
> which most browsers and the Hugging Face preview cannot decode — so the tactile videos do not show
> thumbnails on the dataset page. This is expected; decode them locally (ffmpeg / PyAV / torchcodec).
> The RGB cameras use standard `yuv420p` (limited range, BT.709) and preview normally.

## Usage

### With LeRobot

```python
from lerobot.datasets.lerobot_dataset import LeRobotDataset

ds = LeRobotDataset("zekaiwang/trex_dataset")
frame = ds[0]  # dict of tensors: observation.state, action, observation.tactile_force, images...
```

### Stream individual episodes (no full download)

The [**T-Rex Quick Start**](https://github.com/ZhuoyangLiu2005/T-Rex/tree/main/dataset_quickstart)
companion repo browses, inspects, and replays single episodes without downloading the full dataset,
and includes a notebook you can
[open directly in Colab](https://colab.research.google.com/github/ZhuoyangLiu2005/T-Rex/blob/main/dataset_quickstart/quickstart.ipynb).

## Dataset viewer

The Hugging Face table viewer is manually configured (the `configs` block above) with two views:

- **`frames`** (default) — the raw per-frame `observation.state`, `action`, and
  `observation.tactile_force` arrays (plus index columns). This is the actual data, and what
  `datasets.load_dataset("zekaiwang/trex_dataset")` returns by default.
- **`episodes`** — one row per episode with its language `caption`, `motor_primitive`, `object`, and
  `target`: a readable table of contents. Switch to it with the config dropdown, or load it with
  `datasets.load_dataset("zekaiwang/trex_dataset", "episodes")`.

Videos are stored as separate `.mp4` files referenced by timestamp pointers, so they are not shown in
the table viewer — load them via LeRobot or the quick-start tools. For interactive video playback, use
the LeRobot dataset visualizer.

## Layout

```
data/chunk-000/file-*.parquet     per-frame state / action / tactile_force (+ index columns)
videos/<key>/chunk-*/file-*.mp4   23 video streams (3 RGB + 20 tactile)
episodes_preview.parquet          curated per-episode labels for the web viewer (see "Dataset viewer")
meta/info.json                    features, shapes, fps, codebase_version
meta/episodes/*.parquet           per-episode metadata + stats + video pointers
meta/tasks.parquet                task (caption) table
meta/stats.json                   global feature statistics
```

## Citation

If you find the T-Rex Dataset useful, please cite:

```bibtex
@misc{trex2026,
  title={T-Rex: Tactile-Reactive Dexterous Manipulation}, 
  author={Dantong Niu and Zhuoyang Liu and Zekai Wang and Boning Shao and Zhao-Heng Yin and Anirudh Pai and Yuvan Sharma and Stefano Saravalle and Ruijie Zheng and Jing Wang and Ryan Punamiya and Mengda Xu and Yuqi Xie and Yunfan Jiang and Letian Fu and Konstantinos Kallidromitis and Matteo Gioia and Junyi Zhang and Jiaxin Ge and Haiwen Feng and Fabio Galasso and Wei Zhan and David M. Chan and Yutong Bai and Roei Herzig and Jiahui Lei and Fei-Fei Li and Ken Goldberg and Jitendra Malik and Pieter Abbeel and Yuke Zhu and Danfei Xu and Jim Fan and Trevor Darrell},
  year={2026},
  eprint={2606.17055},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2606.17055}, 
}
```

## License

Released under the **MIT License** © 2026 The Regents of the University of California. See `LICENSE`.