DexCatch: Learning to Catch Arbitrary Objects with Dexterous Hands

Fengbo Lan, Shengjie Wang, Yunzhe Zhang, Haotian Xu, Oluwatosin OluwaPelumi Oseni, Ziye Zhang, Yang Gao, Tao Zhang

Keywords: Reinforcement Learning, Dexterous Manipulation, System Stability

TL;DR: We propose a Learning-based framework for Throwing-Catching tasks using dexterous hands (LTC).

Abstract: Achieving human-like dexterous manipulation remains a crucial area of research in robotics. Current research focuses on improving the success rate of pick-and-place tasks. Compared with pick-and-place, throwing-catching behavior has the potential to increase the speed of transporting objects to their destination. However, dynamic dexterous manipulation poses a major challenge for stable control due to a large number of dynamic contacts. In this paper, we propose a Learning-based framework for Throwing-Catching tasks using dexterous hands (LTC). Our method, LTC, achieves a 73% success rate across 45 scenarios (diverse hand poses and objects), and the learned policies demonstrate strong zero-shot transfer performance on unseen objects. Additionally, in tasks where the object in hand faces sideways, an extremely unstable scenario due to the lack of support from the palm, all baselines fail, while our method still achieves a success rate of over 60%.

Student Paper: yes

Submission Number: 294