Stick Roller: Precise In-hand Stick Rolling with a Sample-Efficient Tactile Model

Yipai Du, Pokuang Zhou, Michael Yu Wang, Wenzhao Lian, Yu She

Abstract: In-hand manipulation is challenging in robotics due to the intricate contact dynamics and high degrees of control freedom. Precise manipulation with high accuracy often requires tactile perception, which adds further complexity to the system. Despite the challenges in perception and control, the rolling stick problem is an essential and practical motion primitive with many demanding industrial applications. This work aims to learn the high-resolution tactile dynamics of the rolling stick. Specifically, we try manipulating a small stick using the Allegro hand equipped with the Digit vision-based tactile sensor. The learning framework includes an action filtering module, tactile perception module, and learning with uncertainty module, all designed to operate in low data regimes. With only 2.3% amount of data and 5.7% model complexity of previous similar work, our learned contact dynamics model achieves better grasp stability, sub-millimeter precision, and promising zero-shot generalizability across novel objects. The proposed framework demonstrates the potential for precise in-hand manipulation with tactile feedback on real hardware. The project source code is available at: https://github.com/duyipai/Allegro_Digit. A video presentation is available here.

External IDs:dblp:conf/iros/DuZWLS24