# UniTacHand: Unified Spatio-Tactile Representation for Human to Robotic Hand Skill Transfer

arXiv: https://arxiv.org/abs/2512.21233 · HTML: https://mlsanity.com/paper/2512.21233/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2025.

## Built on by (most cited first, 5 of 5)

- [Human-Centric Transferable Tactile Pre-Training for Dexterous Robotic Manipulation](https://mlsanity.com/paper/2607.01067/) (2026)
- [EgoTac: In-the-wild Tactile Prediction from Egocentric Vision](https://mlsanity.com/paper/2608.15060/) (2026)
- [The Embodiment Gap in Robot Foundation Models](https://mlsanity.com/paper/2608.18433/) (2026)
- [SoTa: Soft Tactile Skins for Dexterous Manipulation](https://mlsanity.com/paper/2610.02338/) (2026)
- [TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning](https://mlsanity.com/paper/2610.11945/) (2026)

## Abstract (arXiv)

Tactile sensing is crucial for robotic hands to achieve human-level dexterous manipulation, especially in scenarios with visual occlusion. However, its application is often hindered by the difficulty of collecting large-scale real-world robotic tactile data. In this study, we propose to collect low-cost human manipulation data using haptic gloves for tactile-based robotic policy learning. The misalignment between human and robotic tactile data makes it challenging to transfer policies learned from human data to robots. To bridge this gap, we propose UniTacHand, a unified representation to align robotic tactile information captured by dexterous hands with human hand touch obtained from gloves. First, we project tactile signals from both human hands and robotic hands onto a morphologically consistent 2D surface space of the MANO hand model. This unification standardizes the heterogeneous data structures and inherently embeds the tactile signals with spatial context. Then, we introduce a contrastive learning method to align them into a unified latent space, trained on only 10 minutes of paired data from our data collection system. Our approach enables zero-shot tactile-based policy transfer from humans to a real robot, generalizing to objects unseen in the pre-training data. We also demonstrate that co-training on mixed data, including both human and robotic demonstrations via UniTacHand, yields better performance and data efficiency compared with using only robotic data. UniTacHand paves a path toward general, scalable, and data-efficient learning for tactile-based dexterous hands.