人形机器人移动与操作的任务与运动规划
Task and Motion Planning for Humanoid Loco-manipulation
AI总结：
本文提出了一种基于优化的任务与运动规划框架，通过共享接触模式实现人形机器人移动与操作的统一规划。
摘要：
本文提出了一种基于优化的任务与运动规划（TAMP）框架，通过共享接触模式的表示来统一规划移动和操作。我们将符号动作定义为接触模式的变化，将高层规划 grounded 在底层运动上。这使我们能够实现一个涵盖任务、接触和运动规划的统一搜索，同时纳入全身动力学以及机器人、被操作物体和环境之间的所有约束。在人形平台上结果表明，我们的方法能够生成在长动作序列中需要复杂推理的物理一致的移动与操作行为。据我们所知，这是首个能够解决集成TAMP公式化问题的工作，该问题具有完全无环规划和全身动力学以及执行约束的人形移动与操作问题。
英文摘要：
This work presents an optimization-based task and motion planning (TAMP) framework that unifies planning for locomotion and manipulation through a shared representation of contact modes. We define symbolic actions as contact mode changes, grounding high-level planning in low-level motion. This enables a unified search that spans task, contact, and motion planning while incorporating whole-body dynamics, as well as all constraints between the robot, the manipulated object, and the environment. Results on a humanoid platform show that our method can generate a broad range of physically consistent loco-manipulation behaviors over long action sequences requiring complex reasoning. To the best of our knowledge, this is the first work that enables the resolution of an integrated TAMP formulation with fully acyclic planning and whole body dynamics with actuation constraints for the humanoid loco-manipulation problem.
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。
