We present a hierarchical planning framework for dexterous robotic manipulation (HiDex). This framework exploits in-hand and extrinsic dexterity by actively exploring contacts. It generates rigid-body motions and complex contact sequences. Our framework is based on Monte-Carlo Tree Search (MCTS) and has three levels: 1) planning object motions and environment contact modes; 2) planning robot contacts; 3) path evaluation and control optimization that passes the rewards to the upper levels. 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 could provide 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 of them on two robot platforms.

翻译：我们提出了一种用于灵巧机器人操作的层级化规划框架（HiDex）。该框架通过主动探索接触行为，充分利用手指内在灵巧性与环境外在灵巧性，生成刚体运动与复杂接触序列。本框架基于蒙特卡洛树搜索（MCTS）构建，包含三个层级：1）规划物体运动与环境接触模式；2）规划机器人接触行为；3）路径评估与控制优化，将奖励信号传递至上层。该框架具有两大优势：首先，它能在接触产生的高维复杂空间中进行高效全局推理，可在数秒内解决需要内在灵巧性（利用手指）与外在灵巧性（利用环境）的多样化操作任务；其次，框架允许融入专家知识与可定制的任务力学模型，仅需少量修改即可适配不同场景与机器人平台。因此，它为多种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种手部构型与15种场景下的实验结果，并在两个机器人平台上演示了其中8种场景。