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Planning robot dexterity is challenging due to the non-smoothness introducedby contacts, intricate fine motions, and ever-changing scenarios. We present ahierarchical planning framework for dexterous robotic manipulation (HiDex).This framework explores in-hand and extrinsic dexterity by leveraging contacts.It generates rigid-body motions and complex contact sequences. Our framework isbased on Monte-Carlo Tree Search and has three levels: 1) planning objectmotions and environment contact modes; 2) planning robot contacts; 3) pathevaluation and control optimization. This framework offers two main advantages.First, it allows efficient global reasoning over high-dimensional complex spacecreated by contacts. It solves a diverse set of manipulation tasks that requiredexterity, both intrinsic (using the fingers) and extrinsic (also using theenvironment), mostly in seconds. Second, our framework allows the incorporationof expert knowledge and customizable setups in task mechanics and models. Itrequires minor modifications to accommodate different scenarios and robots.Hence, it provides a flexible and generalizable solution for variousmanipulation tasks. As examples, we analyze the results on 7 handconfigurations and 15 scenarios. We demonstrate 8 tasks on two robot platforms.

Enhancing Dexterity in Robotic Manipulation via Hierarchical Contact Exploration