README.md

---
license: cc-by-nc-4.0
language: [en]
pretty_name: EgoTactile
tags: [tactile, tactile-sensing, egocentric-vision, grasp-pressure, robotics, computer-vision, video, multimodal]
---

# EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video

This repository contains the official dataset for:

> **EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video**  
> **ICML 2026 Spotlight**

- Paper: https://arxiv.org/abs/2606.09243
- Project Page: https://egotactile.github.io/

## Dataset Summary

**EgoTactile** is a large-scale benchmark that pairs egocentric RGB video with synchronized full-hand pressure measurements during everyday object grasping.

The dataset is designed to support research on estimating dynamic grasp pressure from visual observations. This task is challenging because hand-object contact regions are frequently occluded, while visually similar grasping observations may correspond to different pressure distributions.

EgoTactile includes:

- 12 participants
- 63 everyday objects
- 7 object categories
- Egocentric RGB video recorded at 1280 × 720 resolution and 15 FPS
- Full-hand tactile pressure measurements from 162 sensing locations
- Pressure measurements within a 0–350 N range
- Object and participant metadata
- Temporally synchronized visual and tactile streams at 15 Hz
- A bare-hand subset for evaluating transfer to natural hand appearances

## Associated Methods

The accompanying paper introduces two methods evaluated on EgoTactile:

- **EgoPressureFormer**, a discriminative baseline for full-hand grasp-pressure estimation from egocentric video.
- **EgoPressureDiff**, a conditional diffusion framework that adapts a pretrained video diffusion backbone for pressure estimation under partial visual observations and physical ambiguity.

Please refer to the paper for complete methodological and experimental details.

## Dataset Organization

The dataset consists of two primary subsets corresponding to different acquisition protocols.

### 1. Gloved-Hand Set

The Gloved-Hand Set contains synchronized egocentric RGB videos and tactile pressure measurements collected while participants wear the tactile sensing glove.

#### Purpose

This subset provides direct supervision for learning mappings from egocentric video observations to full-hand pressure distributions.

#### Contents

- Egocentric RGB video frames
- Synchronized 162-dimensional pressure measurements
- Object metadata
- Anonymized participant metadata
- Temporal and sequence identifiers

### 2. Bare-Hand Set

The Bare-Hand Set is designed to evaluate transfer to natural hand appearances without a visible tactile glove.

During data acquisition, the hand visible to the egocentric camera is bare, while a synchronized off-camera gloved hand performs the corresponding grasping action and provides the tactile pressure reference. The two actions are coordinated using metronome guidance.

#### Purpose

This subset supports evaluation of transfer from instrumented gloved-hand observations to natural bare-hand scenarios.

#### Contents

- Egocentric RGB videos of bare-hand grasping
- Synchronized tactile pressure references
- Object metadata
- Anonymized participant metadata
- Temporal and sequence identifiers

## Modalities

### Egocentric RGB Video

- Resolution: 1280 × 720
- Frame rate: 15 FPS
- Viewpoint: head-mounted egocentric camera
- Content: hand-object grasping interactions

### Tactile Pressure

- Number of sensing locations: 162
- Sampling rate: 15 Hz after synchronization
- Pressure range: 0–350 N
- Coverage: full-hand tactile sensing
- Alignment: temporally synchronized with the RGB video stream

### Object Metadata

Object metadata includes:

- Object name
- Object category
- Weight
- Surface material
- Fill state

### Participant Metadata

Participant metadata includes:

- Anonymized participant ID (`p001`–`p012`)
- Gender
- Hand length

Participant identities are not included in the released dataset.

## Intended Uses

EgoTactile is intended for research in areas including:

- Grasp-pressure estimation
- Vision-based tactile inference
- Egocentric hand-object interaction understanding
- Multimodal representation learning
- Tactile sensing
- Robotic manipulation
- Human-robot interaction
- Transfer from instrumented hands to natural bare hands

## Limitations

Users should consider the following limitations:

- The dataset contains a finite set of participants, objects, and grasping behaviors.
- Data were collected under controlled acquisition conditions.
- Pressure references in the Bare-Hand Set are obtained through synchronized paired actions rather than direct sensing on the visible bare hand.
- Performance on unseen environments, camera configurations, object types, and manipulation behaviors may differ from the reported benchmark results.
- Participant-level attributes should not be used for identity inference or unintended profiling.

## License

EgoTactile is released under the **Creative Commons Attribution-NonCommercial 4.0 International License**, abbreviated as **CC BY-NC 4.0**.

The dataset may be used for non-commercial research purposes with appropriate attribution. Users are responsible for complying with the license terms.

## Citation

Please cite the following paper when using EgoTactile:

```bibtex
@article{zeng2026egotactile,
  title   = {EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video},
  author  = {Zeng, Yuan and Shi, Yujia and Tan, Tiao and Li, Xingting and Qin, Yaqi and Lu, Zongqing and Yang, Wenming and Xue, Jing-Hao and Liao, Qingmin},
  journal = {arXiv preprint arXiv:2606.09243},
  year    = {2026}
}
```

## Contact

For questions about the dataset, benchmark, or accompanying paper, please refer to the contact information provided on the project page or open an issue in the corresponding public repository.