ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A
Kai Gao, Zhaxizhuoma, Yan Ding, Shiqi Zhang, Jingjin Yu
Abstract
Effectively performing object rearrangement is an essential skill for mobile manipulators, e.g., setting up a dinner table. A key challenge in such problems is deciding an appropriate ordering to effectively untangle object-object dependencies while considering the necessary motions for realizing manipulation tasks (e.g., pick and place). Computing time-optimal multi-object rearrangement solutions for mobile manipulators remains a largely untapped research direction. In this work, we propose ORLA*, which leverages delayed/lazy evaluation in searching for a high-quality object pick-n-place sequence that considers both end-effector and mobile robot base travel. ORLA* readily handles multi-layered rearrangement tasks powered by learning-based stability predictions. 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: gaokai15.github.io/ORLA-Star/
BibTeX
@inproceedings{icra2025_orlamobilemanipu,
title = {ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A},
author = {Kai Gao and Zhaxizhuoma and Yan Ding and Shiqi Zhang and Jingjin Yu},
booktitle = {ICRA 2025},
year = {2025}
}