用程序搜索与元优化提升机器人任务规划与运动控制的协同效率。
Meta-Optimization and Program Search using Language Models for Task and Motion Planning
- 通过程序搜索连接高层规划与底层轨迹优化
- 零阶方法优化模型输出参数，提升执行精度
- 在复杂操作与绘图任务中优于现有方法
智能体与真实世界的交互需要同时推理高层计划与底层控制。任务与运动规划（TAMP）通过结合符号规划与连续轨迹生成来解决此问题。近期基于基础模型的TAMP方法已取得显著成果，包括快速规划时间与执行自然语言指令的能力。然而，高层规划与底层运动生成之间的最优接口仍待探索：先前方法受限于过度抽象（如拼接简化技能动作）或缺乏抽象（如直接预测关节角度）。本文提出一种新方法，通过元优化解决这些问题：(i) 使用程序搜索作为基础模型与机器人控制之间的接口，对轨迹优化问题进行搜索；(ii) 采用零阶方法优化基础模型输出中的数值参数。在具有挑战性的物体操作和绘图任务上，实验结果表明该方法优于先前的TAMP方法。
原文摘要 · Abstract (English)
Intelligent interaction with the real world requires robotic agents to jointly reason over high-level plans and low-level controls. Task and motion planning (TAMP) addresses this by combining symbolic planning and continuous trajectory generation. Recently, foundation model approaches to TAMP have presented impressive results, including fast planning times and the execution of natural language instructions. Yet, the optimal interface between high-level planning and low-level motion generation remains an open question: prior approaches are limited by either too much abstraction (e.g., chaining simplified skill primitives) or a lack thereof (e.g., direct joint angle prediction). Our method introduces a novel technique employing a form of meta-optimization to address these issues by: (i) using program search over trajectory optimization problems as an interface between a foundation model and robot control, and (ii) leveraging a zero-order method to optimize numerical parameters in the foundation model output. Results on challenging object manipulation and drawing tasks confirm that our proposed method improves over prior TAMP approaches.
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