README.md

---
annotations_creators:
- expert-generated
language:
- en
license: mit
size_categories:
- 10K<n<100K
task_categories:
- robotics
- other
pretty_name: FreeTacMan (FiftyOne)
tags:
- fiftyone
- group
- video
- robotics
- manipulation
- tactile
- visuo-tactile
- imitation-learning
---

# Dataset Card for FreeTacMan (FiftyOne)

![FiftyOne](https://img.shields.io/badge/FiftyOne-grouped%20video-orange)

![image/png](freetacman.gif)

**FreeTacMan (FiftyOne)** is a grouped FiftyOne video dataset built from the
[OpenDriveLab/FreeTacMan](https://huggingface.co/datasets/OpenDriveLab/FreeTacMan)
visuo-tactile manipulation dataset. Each group is a single demonstration
trajectory, with one video per camera/sensor view and the full tool-center-point
(TCP) and gripper trajectory stored as per-frame fields.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset from the Hugging Face Hub
dataset = load_from_hub("Voxel51/FreeTacMan")

# Launch the App
session = fo.launch_app(dataset)
```

## Dataset Details

### Dataset Description

FreeTacMan is a large-scale, high-precision **visuo-tactile** manipulation
dataset collected with a robot-free, human-operated data collection system. It
targets contact-rich manipulation, pairing synchronized RGB video from a
wrist-mounted fisheye camera with two GelSight-style optical **tactile
sensors**, plus dense 6-DoF end-effector trajectories. The source dataset
contains over 3,000k visuo-tactile image pairs and more than 10k trajectories
across 50 task categories.

This FiftyOne version reorganizes the source data into a **grouped video
dataset** so the tactile and visual streams for a demonstration can be viewed
side by side in the FiftyOne App, with the robot proprioception rendered as
per-frame numeric fields.

- **Curated by:** [OpenDriveLab](https://opendrivelab.com/)
- **Funded by:** [More Information Needed]
- **Shared by:** Longyan Wu, Checheng Yu, Jieji Ren, Li Chen, Yufei Jiang, Ran Huang, Guoying Gu, Hongyang Li
- **Language(s):** N/A (video, tactile, and trajectory data; no text)
- **License:** MIT (source dataset license)

### Dataset Sources

- **Repository:** <https://github.com/OpenDriveLab/FreeTacMan>
- **Paper:** <https://arxiv.org/abs/2506.01941> — *FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation* (ICRA 2026)

## Uses

### Direct Use

This dataset is intended for research on visuo-tactile robot learning,
including:
- Imitation learning of contact-rich manipulation policies (e.g. Action
  Chunking Transformer-style models) that fuse wrist-camera RGB with optical
  tactile sensor observations and 6-DoF end-effector trajectories.
- Tactile representation / contrastive pretraining of a tactile encoder
  aligned with a visual encoder, as described in the source paper.
- Benchmarking tactile-vs-vision-only policies on contact-rich tasks (e.g.
  slip detection, force-sensitive grasping, precise insertion).

### Out-of-Scope Use

[More Information Needed]. The source paper notes the tactile sensor is an
optical (camera-based) gel-deformation sensor rather than a calibrated
force/torque sensor, so the dataset should not be treated as ground-truth
contact force measurements. The paper's own experiments were validated on a
PIPER 6-DoF arm; direct transfer of learned policies to other embodiments has
not been established by the source paper.

## Dataset Structure

**Dataset name:** `FreeTacMan`

**Media type:** `group`

**Default group slice:** `tactile_sensor_1`

### Summary

| Property | Value |
| --- | --- |
| Groups (trajectories) | 6,228 |
| Video samples (total) | 18,084 |
| Tasks | 45 |
| Group slices | `tactile_sensor_1`, `tactile_sensor_2`, `fisheye_camera` |

### Groups and slices

Each group is one demonstration trajectory. The linked slices are the
synchronized streams recorded during that demonstration:

| Slice | Media type | Samples | Description |
| --- | --- | --- | --- |
| `tactile_sensor_1` (default) | video | 6,228 | First optical tactile sensor stream |
| `tactile_sensor_2` | video | 6,228 | Second optical tactile sensor stream |
| `fisheye_camera` | video | 5,628 | Wrist-mounted fisheye RGB camera stream |

`fisheye_camera` is missing from ~600 groups because a small subset of tasks
were recorded with only the two tactile-sensor streams and no wrist camera.
Switch slices in the FiftyOne App to compare the tactile imprints against the
RGB view for the same demonstration. Videos are transcoded to H.264 /
`yuv420p` for in-App playback; the source videos are MPEG-4 Part 2.

### Sample-level fields

| Field | Type | Description |
| --- | --- | --- |
| `trajectory_id` | string | Unique id, e.g. `Hold_3` (`{task}_{demo_idx}`) |
| `task` | `fo.Classification` | Task name, e.g. `PourWater` |
| `demo_idx` | int | Demonstration index within the task |
| `camera` | string | Original source camera id for this sample (`camera1`/`camera2`/`camera3`; `camera1` maps to `tactile_sensor_1`, `camera2` to `tactile_sensor_2`, `camera3` to `fisheye_camera`) |
| `num_timesteps` | int | Number of trajectory timesteps for this demo |

### Frame-level fields

The 6-DoF end-effector trajectory is replicated on every stream in the group,
so proprioception can be read off any slice frame-by-frame. Per the source
paper, these values come from an OptiTrack motion-capture system tracking the
in-situ gripper interface at 300 Hz, downsampled and synchronized to the 30 Hz
camera frame rate:

| Field | Type | Description |
| --- | --- | --- |
| `timestamp` | float | Unix timestamp of the frame |
| `tcp_pos_x` / `tcp_pos_y` / `tcp_pos_z` | float | TCP (tool center point) position |
| `tcp_euler_x` / `tcp_euler_y` / `tcp_euler_z` | float | TCP orientation (Euler angles) |
| `quat_w` / `quat_x` / `quat_y` / `quat_z` | float | TCP orientation (quaternion) |
| `gripper_distance` | float | Gripper opening distance |

### Tasks

This dataset spans 45 contact-rich manipulation tasks, including
`ArrangeFruit`, `CakePiping`, `CutBanana`, `PourWater`, `ScrewInTheBulb`,
`SqueezeToothpaste`, `ThreadNeedle`, `UsbPlug`, `WipeBoard`, `Write`, and more.

```python
from fiftyone import ViewField as F

# All demonstrations of a single task
pour = dataset.match(F("task.label") == "PourWater")

# Just the tactile view for every demonstration
tactile = dataset.select_group_slices("tactile_sensor_1")
```

## Dataset Creation

### Curation Rationale

Existing demonstration-collection setups for contact-rich manipulation are
either expensive/complex real-robot teleoperation rigs (motion-capture, VR/AR,
primary-replica arms) or handheld interfaces whose multi-link trigger-based
grippers introduce mechanical backlash that blurs tactile cues. FreeTacMan was
built to eliminate that backlash: a wearable, in-situ visuo-tactile sensor
sits directly at the operator's fingertip, giving zero-mechanical-attenuation
tactile feedback while remaining robot-free and cross-embodiment. The dataset
was curated by collecting many contact-rich manipulation demonstrations with
this system to support tactile-conditioned imitation learning research.

### Source Data

#### Data Collection and Processing

Each demonstration frame pairs a wrist-mounted fisheye-camera RGB image
(180° field of view, 640×480 @ 30 FPS) with two camera-based optical tactile
sensor images (640×480 @ 30 FPS, one per fingertip), plus the 6-DoF end
-effector pose and gripper width. End-effector pose is derived from an
OptiTrack motion-capture system tracking five retroreflective markers on the
in-situ gripper interface at 300 Hz (mean tracking error 0.118 mm), with
marker coordinates transformed into the robot base frame and downsampled to
synchronize with the 30 Hz camera streams.

For this FiftyOne release, the source per-task directories of
`{task}_{demo_idx}_camera{1,2,3}.mp4` videos and `{task}_{demo_idx}_traj.csv`
trajectory files were parsed into a grouped video dataset: each trajectory
CSV's 12 columns were mapped to per-frame fields, source MPEG-4 Part 2 videos
were transcoded to H.264/`yuv420p` MP4 for in-App playback, and each
demonstration's camera streams were joined into a `fo.Group()` with slices
renamed to `tactile_sensor_1`, `tactile_sensor_2`, and `fisheye_camera`.

#### Who are the source data producers?

Demonstrations were collected by human operators wearing/holding the
FreeTacMan wearable interface. The source paper's user study describes 12
volunteer operators collecting demonstrations across 8 tasks to evaluate the
data-collection system itself; [More Information Needed] on the exact number
and identity of operators who produced the full 45-task, 6,228-trajectory
dataset released publicly, as this is a larger-scale release beyond the
paper's user study.

### Annotations

#### Annotation process

There is no manual human annotation. The `task` label is assigned from the
source directory/file naming convention (one folder per task). All frame
-level trajectory fields (`tcp_pos_*`, `tcp_euler_*`, `quat_*`,
`gripper_distance`, `timestamp`) are captured automatically by the OptiTrack
motion-capture system described above, not hand-labeled.

#### Who are the annotators?

Not applicable — trajectory fields are sensor/motion-capture output rather
than human annotations. [More Information Needed] on task-label assignment
beyond the directory-naming convention.

#### Personal and Sensitive Information

[More Information Needed]. The source paper does not discuss personal or
sensitive information; recordings are of a wrist-mounted camera and
fingertip-mounted tactile sensors during tabletop manipulation tasks.

## Citation

If you use the source FreeTacMan dataset, cite:

**BibTeX:**

```bibtex
@article{wu2025freetacman,
  title   = {FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation},
  author  = {Wu, Longyan and Yu, Checheng and Ren, Jieji and Chen, Li and Jiang, Yufei and Huang, Ran and Gu, Guoying and Li, Hongyang},
  journal = {IEEE International Conference on Robotics and Automation (ICRA)},
  year    = {2026}
}
```

**APA:**

Wu, L., Yu, C., Ren, J., Chen, L., Jiang, Y., Huang, R., Gu, G., & Li, H. (2026). FreeTacMan: Robot-free visuo-tactile data collection system for contact-rich manipulation. *IEEE International Conference on Robotics and Automation (ICRA)*.

## More Information

- Project page: <http://opendrivelab.com/freetacman>
- Code: <https://github.com/OpenDriveLab/FreeTacMan>
- Hardware guide: <https://docs.google.com/document/d/1Hhi2stn_goXUHdYi7461w10AJbzQDC0fdYaSxMdMVXM/edit>
- Video: <https://opendrivelab.github.io/FreeTacMan/landing/FreeTacMan_demo_video.mp4>
- Source dataset contact: Longyan Wu (im.longyanwu@gmail.com)

## Dataset Card Authors

[Harpreet Sahota](https://huggingface.co/harpreetsahota) (FiftyOne conversion and card)