Task-Driven Co-Design of Mobile Manipulators

TL;DR

This paper introduces a novel concurrent design method for mobile manipulators that optimizes arm-mounting parameters using reinforcement learning and Bayesian optimization, leading to improved task performance and practical, modular robot designs.

Contribution

It presents the first concurrent design approach combining reinforcement learning with Bayesian optimization for mobile manipulators, enhancing task-specific performance.

Findings

Optimized designs outperform heuristic-based configurations.

Designed manipulators show improved performance on household tasks.

Proposed method produces practical, modular, and affordable robot configurations.

Abstract

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.