---
license: mit
task_categories:
  - robotics
  - video-classification
  - feature-extraction
tags:
  - tactile
  - visuotactile
  - gelsight
  - video
  - pretraining
  - self-supervised
  - robot-manipulation
size_categories:
  - 10K<n<100K
pretty_name: Tactile Video Pretrain
configs:
  - config_name: tactile_only
    data_files:
      - split: train
        path: tactile_only/train-*.parquet
  - config_name: tactile_rgb
    data_files:
      - split: train
        path: tactile_rgb/train-*.parquet
---

# Tactile Video Pretrain (`yxma/tactile-video-pretrain`)

**~48 hours of GelSight-style tactile video, aggregated across 4 public sources.** Each row is one demonstration / episode / scan / touch session. Two configs share one shared video folder:

- **`tactile_only`** — one tactile stream per row. Use for tactile-only
  self-supervised pretraining (video MAE / V-JEPA / contrastive / masked).
- **`tactile_rgb`** — paired (tactile, scene-RGB) videos per row. Use for
  cross-modal alignment (CLIP-style, contrastive, masked cross-modal).

![summary](assets/summary.png)

## Quick start

```python
from datasets import load_dataset

# tactile-only stream
ds = load_dataset("yxma/tactile-video-pretrain", "tactile_only", split="train")
print(ds[0]["video"])           # repo-relative path
print(ds[0]["source"])          # "freetacman" | "gelslam_tracking" | ...

# paired tactile + RGB
ds2 = load_dataset("yxma/tactile-video-pretrain", "tactile_rgb", split="train")
print(ds2[0]["tactile_video"], ds2[0]["rgb_video"])
```

Resolve repo-relative paths to local files:

```python
from huggingface_hub import snapshot_download
import os
root = snapshot_download("yxma/tactile-video-pretrain", repo_type="dataset")
local = os.path.join(root, ds[0]["video"])
```

## Composition

| source | clips | hours | tactile resolution | RGB pair? |
|---|---:|---:|---|---|
| **freetacman** | 11,967 | **29.71** | 640×480 @ 30 FPS | ✅ 11,965 |
| **touchandgo** | 140 | **14.66** | 640×480 @ 27 FPS | ✅ 138 |
| **gelslam_recon** | 15 | **2.47** | 320×240 @ 25 FPS | – |
| **gelslam_tracking** | 140 | **0.81** | 320×240 @ 25 FPS | – |
| **TOTAL** | **12,262** | **47.65** | mixed | 12,103 |

![source pie](assets/source_pie.png)

![composition](assets/composition.png)

| | |
|---|---|
| Total frames | 4,929,371 |
| Mean clip length | 14 s |
| Median clip length | 8 s |
| Codec | H.264 (re-encoded for browser playback) |

![duration distribution](assets/duration_distribution.png)

![resolution distribution](assets/resolution_distribution.png)

## Preview clips

Short (≤ 8 s) MP4 previews of one randomly-picked clip per source.

<table>
<tr>
<td><b>freetacman tactile</b></td>
<td><b>freetacman tactile + scene</b></td>
</tr>
<tr>
<td><video controls width="320" loop muted src="https://huggingface.co/datasets/yxma/tactile-video-pretrain/resolve/main/assets/preview_freetacman.mp4"></video></td>
<td><video controls width="480" loop muted src="https://huggingface.co/datasets/yxma/tactile-video-pretrain/resolve/main/assets/preview_freetacman_pair.mp4"></video></td>
</tr>
<tr>
<td><b>touchandgo tactile</b></td>
<td><b>touchandgo tactile + scene</b></td>
</tr>
<tr>
<td><video controls width="320" loop muted src="https://huggingface.co/datasets/yxma/tactile-video-pretrain/resolve/main/assets/preview_touchandgo.mp4"></video></td>
<td><video controls width="480" loop muted src="https://huggingface.co/datasets/yxma/tactile-video-pretrain/resolve/main/assets/preview_touchandgo_pair.mp4"></video></td>
</tr>
<tr>
<td><b>gelslam_tracking</b></td>
<td><b>gelslam_recon (long surface scan)</b></td>
</tr>
<tr>
<td><video controls width="320" loop muted src="https://huggingface.co/datasets/yxma/tactile-video-pretrain/resolve/main/assets/preview_gelslam_tracking.mp4"></video></td>
<td><video controls width="320" loop muted src="https://huggingface.co/datasets/yxma/tactile-video-pretrain/resolve/main/assets/preview_gelslam_recon.mp4"></video></td>
</tr>
</table>

## Sample frames

Per-source 40-frame grids:

| | |
|---|---|
| **freetacman** tactile | **touchandgo** tactile |
| ![](assets/samples_40_freetacman.png) | ![](assets/samples_40_touchandgo.png) |
| **gelslam_tracking** tactile | **gelslam_recon** tactile |
| ![](assets/samples_40_gelslam_tracking.png) | ![](assets/samples_40_gelslam_recon.png) |

Paired tactile + scene RGB (16 random pairs):

![pairs](assets/samples_pairs.png)

## Schema

### `tactile_only`

| column | type | description |
|---|---|---|
| `clip_id` | string | stable per-clip id, format depends on source |
| `source` | string | one of `freetacman`, `gelslam_tracking`, `gelslam_recon`, `touchandgo` |
| `task` | string | task name where applicable (FreeTacMan) |
| `object_class` | string | object class (GelSLAM) |
| `demo_id` | int32 | demonstration / episode id |
| `side` | string | `left`/`right` (FreeTacMan); else null |
| `n_frames` | int32 | number of video frames |
| `fps` | float32 | source frame rate |
| `duration_s` | float32 | `n_frames / fps` |
| `width`, `height` | int32 | spatial resolution |
| `has_pose` | bool | true ⇒ GelSLAM tracking 6-DoF poses available |
| `has_gradient` | bool | true ⇒ GelSLAM tracking gradient maps available |
| `has_contact_mask` | bool | true ⇒ GelSLAM tracking contact masks available |
| `video` | string | repo-relative path to MP4 |
| `trajectory` | nullable struct | per-frame state, schema depends on source |

### `tactile_rgb`

Same schema with `video` → `tactile_video`, plus `rgb_video`.

### `trajectory` struct (nullable)

The struct columns differ per source — every field is optional. Convenient quick filter: `row["trajectory"] is None` ⇒ no per-frame state available.

| field | sources |
|---|---|
| `timestamp`, `TCP_pos_*`, `TCP_euler_*`, `quat_*`, `gripper_distance` | freetacman |
| `time_tactile`, `time_scene` | touchandgo |
| (none) | gelslam_recon |
| 6-DoF poses, contact masks, gradient maps | gelslam_tracking — *stored alongside videos as `.npy`, not in parquet* |

## Sources

See **[SOURCES.md](SOURCES.md)** for per-source details (license, sensor, processing recipe) and **[SHORTLIST.md](SHORTLIST.md)** for the full roadmap.

| source | sensor | license | upstream |
|---|---|---|---|
| **FreeTacMan** | McTac-derived GelSight-style | MIT | [OpenDriveLab/FreeTacMan](https://huggingface.co/datasets/OpenDriveLab/FreeTacMan) |
| **GelSLAM** (tracking + reconstruction) | GelSight Mini (markerless) | MIT | [joehjhuang/GelSLAM_dataset](https://huggingface.co/datasets/joehjhuang/GelSLAM_dataset) |
| **Touch and Go** | GelSight (markered) + ego RGB | CC-BY-4.0 | [project page](https://touch-and-go.github.io/) |

## Investigated but not included

- **AgiBot World** — audited 7 spreadsheet-flagged "tactile gripper" tasks; **0 hours of tactile video** in the public release. Details in [SOURCES.md](SOURCES.md#agibot-world).

## Pipeline (vs. upstream)

1. **Rename / re-organize** to a unified naming convention per source.
2. **Re-encode AVI → MP4** (libx264, CRF 18–20) so browser playback works on
   HF preview. GelSLAM's FFV1-AVI files become MP4 here.
3. **Hardlink where possible** so on-disk usage stays low.
4. **Wrap available per-frame state** as a nullable `trajectory` parquet struct.
5. **Add `clip_id` + `source`** for stable filtering across configs.

## Recommended uses

- **Tactile-only SSL** — video MAE / V-JEPA on `tactile_only`; ~30 h FreeTacMan + ~3 h GelSLAM clean Mini frames.
- **Cross-modal pretrain** — contrastive (tactile ↔ scene) on `tactile_rgb`.
- **Geometry-aware SSL** — `has_pose`/`has_gradient`/`has_contact_mask` filters surface the GelSLAM tracking rows for dense supervision.
- **In-the-wild material reasoning** — filter `source = "touchandgo"`.
- **Sensor-shift robustness** — train markerless, test markered (Touch and Go).

## License

MIT, inherited from all currently included upstream sources.

## Citation

```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},
  year    = {2026}
}
@inproceedings{huang2024gelslam,
  title   = {{GelSLAM}: Long-Horizon Object Tracking with Vision-Based Tactile Sensors},
  author  = {Huang, Hung-Jui and ...},
  year    = {2024}
}
@inproceedings{yang2022touchgo,
  title   = {Touch and Go: Learning from Human-Collected Vision and Touch},
  author  = {Yang, Fengyu and ...},
  booktitle = {NeurIPS Datasets and Benchmarks Track},
  year    = {2022}
}
```