通过分层接触探索提升机器人操作的灵巧性
Enhancing Dexterity in Robotic Manipulation via Hierarchical Contact Exploration
AI总结：
针对机器人灵巧操作规划因接触非平滑性等带来的难题，提出基于蒙特卡洛树搜索的三层分层规划框架HiDex，可高效完成多类内外灵巧操作任务，灵活适配不同场景与机器人。
摘要：
由于接触带来的非平滑性、复杂的精细动作以及不断变化的场景，规划机器人灵巧性极具挑战。我们提出了一种用于机器人灵巧操作的分层规划框架HiDex（分层灵巧操作框架）。该框架利用接触来探索手部内在灵巧性与外在灵巧性，可生成刚体运动和复杂的接触序列。我们的框架基于蒙特卡洛树搜索（Monte-Carlo Tree Search），共分为三层：1）规划物体运动与环境接触模式；2）规划机器人接触；3）路径评估与控制优化。该框架具备两大核心优势：其一，它能够在由接触构成的高维复杂空间中进行高效的全局推理，可解决各类需要灵巧性的操作任务，涵盖内在灵巧（使用手指）与外在灵巧（同时利用环境），多数任务可在数秒内完成。其二，该框架支持融入专家知识，且可针对任务力学与模型进行自定义设置，仅需少量修改即可适配不同场景与机器人，因此为各类操作任务提供了灵活且具备泛化性的解决方案。作为示例，我们分析了7种手部构型与15种场景下的结果，并在两个机器人平台上演示了8项任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
