Planning robot dexterity is challenging due to the non-smoothness introduced by contacts, intricate fine motions, and ever-changing scenarios. We present a hierarchical 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 is based on Monte-Carlo Tree Search and has three levels: 1) planning object motions and environment contact modes; 2) planning robot contacts; 3) path evaluation and control optimization. This framework offers two main advantages. First, it allows efficient global reasoning over high-dimensional complex space created by contacts. It solves a diverse set of manipulation tasks that require dexterity, both intrinsic (using the fingers) and extrinsic (also using the environment), mostly in seconds. Second, our framework allows the incorporation of expert knowledge and customizable setups in task mechanics and models. It requires minor modifications to accommodate different scenarios and robots. Hence, it provides a flexible and generalizable solution for various manipulation tasks. As examples, we analyze the results on 7 hand configurations and 15 scenarios. We demonstrate 8 tasks on two robot platforms.

翻译：规划机器人的灵巧性具有挑战性，这是因为接触引入的非光滑性、复杂的精细运动以及不断变化的场景。我们提出了一种用于灵巧机器人操作的层级式规划框架（HiDex）。该框架通过利用接触来探索手内灵巧性和外源性灵巧性，生成刚体运动与复杂接触序列。我们的框架基于蒙特卡洛树搜索，包含三个层级：1）规划物体运动与环境接触模式；2）规划机器人接触模式；3）路径评估与控制优化。该框架具有两大优势：首先，它能够对接触产生的高维复杂空间进行高效全局推理，在数秒内解决需要内源性（使用手指）和外源性（利用环境）灵巧性的多样化操作任务；其次，该框架允许融入专家知识以及任务力学与模型的可定制化设置，仅需少量修改即可适配不同场景和机器人。因此，它为各类操作任务提供了灵活且可泛化的解决方案。作为示例，我们分析了7种手部配置和15种场景下的结果，并在两个机器人平台上演示了8项操作任务。