统一规划人形机器人行走与操作，实现复杂动作的物理合理生成。
Task and Motion Planning for Humanoid Loco-manipulation
- 用接触状态变化定义符号动作，打通高层任务与底层运动的联系。
- 在长序列动作中生成多种符合物理规律的行走操作行为。
- 首次实现全身体动力学下无环规划，适合复杂人形机器人任务。
本文提出一种基于优化的任务与运动规划（TAMP）框架，通过共享的接触模式表示，统一规划行走与操作。将符号动作定义为接触模式的改变，使高层规划扎根于底层运动。该方法可在包含全身动力学和执行器约束的前提下，联合搜索任务、接触与运动规划。在人形机器人平台上的实验表明，该方法能生成一系列长序列、需复杂推理的物理一致的行走操作行为。据我们所知，这是首个实现人形机器人行走-操作一体化TAMP完整无环规划并融合全身体动力学与执行器约束的工作。
原文摘要 · Abstract (English)
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.
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