ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star
This addresses the challenge of efficiently rearranging objects with dependencies for mobile manipulators, representing an incremental advance in robotics planning.
The paper tackles the problem of computing time-optimal multi-object rearrangement for mobile manipulators, such as setting up a dinner table, by proposing ORLA*, which leverages lazy evaluation to find high-quality pick and place sequences considering both end-effector and base travel, and it achieves global optimality through an optimal solver for temporary object placement.
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/