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arXiv 2412.16635cs.RO
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移动机械臂的任务驱动协同设计

Task-Driven Co-Design of Mobile Manipulators

Department of Computer Science, University of Freiburg（计算机科学系，弗赖堡大学）

Raphael Schneider, Daniel Honerkamp, Tim Welschehold, Abhinav Valada

备注：
Accepted for publication at RA-L. Project website: https://moma-codesign.cs.uni-freiburg.de/
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AI总结：

本文提出首个移动机械臂并发设计方法，通过内循环多任务强化学习与外循环BOHB优化协同优化机械臂安装参数，生成在家庭任务中性能显著提升且模块化、经济实用的新型设计。

摘要：

近年来对移动操作的研究兴趣催生了大量新型机器人设计。其中一大类设计聚焦于将现有移动底盘与静态机械臂相结合的模块化平台，它们通过将机械臂以桌面式配置安装来实现模块组合。然而，常见移动操作任务（如打开铰接物体）的工作空间和操作高度与桌面操作任务存在显著差异。因此，这些标准机械臂安装配置可能导致运动学上关节范围和运动受限。为解决这些问题，我们提出了首个面向移动机械臂的并发设计（Concurrent Design）方法，用于优化关键的机械臂安装参数。我们的方法直接针对代表性家庭任务中的任务性能：在内循环中训练一个强大的多任务强化学习策略，在外循环中利用贝叶斯优化与HyperBand（BOHB）对设计配置分布进行优化。由此产生了新颖的设计，在已见和未见的测试任务上均显著提升了性能，并优于基于启发式性能指标生成的设计——这些指标评估成本更低，但与目标运动的关联性较弱。我们评估了所生成设计的物理可行性，结果表明这些设计实用且保持模块化、经济实惠，并与现有商用组件兼容。我们开源了该方法和生成的设计，以促进这些平台的进一步改进。

英文摘要：

Recent interest in mobile manipulation has resulted in a wide range of new robot designs. A large family of these designs focuses on modular platforms that combine existing mobile bases with static manipulator arms. They combine these modules by mounting the arm in a tabletop configuration. However, the operating workspaces and heights for common mobile manipulation tasks, such as opening articulated objects, significantly differ from tabletop manipulation tasks. As a result, these standard arm mounting configurations can result in kinematics with restricted joint ranges and motions. To address these problems, we present the first Concurrent Design approach for mobile manipulators to optimize key arm-mounting parameters. Our approach directly targets task performance across representative household tasks by training a powerful multitask-capable reinforcement learning policy in an inner loop while optimizing over a distribution of design configurations guided by Bayesian Optimization and HyperBand (BOHB) in an outer loop. This results in novel designs that significantly improve performance across both seen and unseen test tasks, and outperform designs generated by heuristic-based performance indices that are cheaper to evaluate but only weakly correlated with the motions of interest. We evaluate the physical feasibility of the resulting designs and show that they are practical and remain modular, affordable, and compatible with existing commercial components. We open-source the approach and generated designs to facilitate further improvements of these 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。

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