# A Humanoid Visual-Tactile-Action Dataset for Contact-Rich Manipulation

arXiv: https://arxiv.org/abs/2510.25725 · HTML: https://mlsanity.com/paper/2510.25725/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2025.

## Built on by (most cited first, 2 of 2)

- [World Model for Robot Learning: A Comprehensive Survey](https://mlsanity.com/paper/2605.00080/) (2026)
- [EgoTactile: Learning Grasp Pressure for Everyday Objects from Egocentric Video](https://mlsanity.com/paper/2606.09243/) (2026)

## Abstract (arXiv)

Contact-rich manipulation has become increasingly important in robot learning. However, previous studies on robot learning datasets have focused on rigid objects and underrepresented the diversity of pressure conditions for real-world manipulation. To address this gap, we present a humanoid visual-tactile-action dataset designed for manipulating deformable soft objects. The dataset was collected via teleoperation using a humanoid robot equipped with dexterous hands, capturing multi-modal interactions under varying pressure conditions. This work also motivates future research on models with advanced optimization strategies capable of effectively leveraging the complexity and diversity of tactile signals.