ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star

TL;DR

This paper introduces ORLA*, a novel algorithm that computes near-optimal object rearrangement sequences for mobile manipulators by combining lazy evaluation, optimal solvers, and machine learning for pile stability, improving efficiency and solution quality.

Contribution

ORLA* is the first approach to integrate lazy search, optimal temporary placement, and machine learning for multi-object rearrangement in mobile manipulation.

Findings

ORLA* outperforms existing methods in simulation tests.

It achieves near-optimal rearrangement sequences efficiently.

The approach effectively handles complex, multi-layered tasks.

Abstract

Effectively performing object rearrangement is an essential skill for mobile manipulators, e.g., setting up a dinner table or organizing a desk. A key challenge in such problems is deciding an appropriate manipulation order for objects to effectively untangle dependencies between objects while considering the necessary motions for realizing the manipulations (e.g., pick and place). To our knowledge, computing time-optimal multi-object rearrangement solutions for mobile manipulators remains a largely untapped research direction. In this research, we propose ORLA*, which leverages delayed (lazy) evaluation in searching for a high-quality object pick and place sequence that considers both end-effector and mobile robot base travel. ORLA* also supports multi-layered rearrangement tasks considering pile stability using machine learning. Employing an optimal solver for finding temporary locations for displacing objects, ORLA* can achieve global optimality. Through extensive simulation and ablation study, we confirm the effectiveness of ORLA* delivering quality solutions for challenging rearrangement instances. Supplementary materials are available at: https://gaokai15.github.io/ORLA-Star/