DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation

TL;DR

DexViTac introduces a portable system for collecting high-fidelity multimodal demonstrations, enabling improved learning of contact-rich dexterous manipulation with high efficiency and robustness in unstructured environments.

Contribution

We developed DexViTac, a novel hardware and algorithmic system for efficient, high-quality multimodal data collection and tactile representation learning for dexterous manipulation.

Findings

Collected over 2,400 demonstrations with high efficiency.

Achieved over 85% success rate in four complex tasks.

Outperformed baseline methods significantly.

Abstract

Large-scale, high-quality multimodal demonstrations are essential for robot learning of contact-rich dexterous manipulation. While human-centric data collection systems lower the barrier to scaling, they struggle to capture the tactile information during physical interactions. Motivated by this, we present DexViTac, a portable, human-centric data collection system tailored for contact-rich dexterous manipulation. The system enables the high-fidelity acquisition of first-person vision, high-density tactile sensing, end-effector poses, and hand kinematics within unstructured, in-the-wild environments. Building upon this hardware, we propose a kinematics-grounded tactile representation learning algorithm that effectively resolves semantic ambiguities within tactile signals. Leveraging the efficiency of DexViTac, we construct a multimodal dataset comprising over 2,400 visuo-tactile-kinematic demonstrations. Experiments demonstrate that DexViTac achieves a collection efficiency exceeding 248 demonstrations per hour and remains robust against complex visual occlusions. Real-world deployment confirms that policies trained with the proposed dataset and learning strategy achieve an average success rate exceeding 85% across four challenging tasks. This performance significantly outperforms baseline methods, thereby validating the substantial improvement the system provides for learning contact-rich dexterous manipulation. Project page: https://xitong-c.github.io/DexViTac/.