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Link to original content: https://doi.org/10.48550/arXiv.2309.13707
[2309.13707] ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star
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Computer Science > Robotics

arXiv:2309.13707 (cs)
[Submitted on 24 Sep 2023 (v1), last revised 20 Oct 2024 (this version, v2)]

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

Authors:Kai Gao, Zhaxizhuoma, Yan Ding, Shiqi Zhang, Jingjin Yu
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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: this https URL
Comments: Submitted to ICRA 2025
Subjects: Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2309.13707 [cs.RO]
  (or arXiv:2309.13707v2 [cs.RO] for this version)
  https://doi.org/10.48550/arXiv.2309.13707

Submission history

From: Kai Gao [view email]
[v1] Sun, 24 Sep 2023 17:40:19 UTC (4,933 KB)
[v2] Sun, 20 Oct 2024 05:23:35 UTC (24,058 KB)
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