mlsanityData to 7 Oct 2026

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

arXiv 2309.13707 ↗First posted 24 Sep 2023cs.RO cs.AI cs.LGcited by 3

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Abstract (arXiv)

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/

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 22 of its 32 references that are in the map. First posted on arXiv 24 Sep 2023.

Builds on

22 of 32 references.

References · 22

  1. 2020ResNeSt: Split-Attention Networkscited by 445
  2. 2020Transporter Networks: Rearranging the Visual World for Robotic Manipulationcited by 342
  3. 2023Task and Motion Planning with Large Language Models for Object Rearrangementcited by 140
  4. 2019Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planningcited by 23
  5. 2020Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulationcited by 16
  6. 2021Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangementcited by 14
  7. 2019Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sortingcited by 12
  8. 2021Active Learning of Abstract Plan Feasibilitycited by 11
  9. 2017Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Graspscited by 9
  10. 2021Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Searchcited by 9
  11. 2021Fast High-Quality Tabletop Rearrangement in Bounded Workspacecited by 9
  12. 2022Persistent Homology for Effective Non-Prehensile Manipulationcited by 8
10 more
  1. 2020DIPN: Deep Interaction Prediction Network with Application to Clutter Removalcited by 7
  2. 2022Visually Grounded Task and Motion Planning for Mobile Manipulationcited by 5
  3. 2022Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangementcited by 5
  4. 2020Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusioncited by 3
  5. 2023Object Reconfiguration with Simulation-Derived Feasible Actionscited by 3
  6. 2023Minimizing Running Buffers for Tabletop Object Rearrangement: Complexity, Fast Algorithms, and Applicationscited by 3
  7. 2016Assembly Sequence Planning for Motion Planningcited by 2
  8. 2022Lazy Rearrangement Planning in Confined Spacescited by 2
  9. 2023Effectively Rearranging Heterogeneous Objects on Cluttered Tabletopscited by 2
  10. 2023Optimal and Stable Multi-Layer Object Rearrangement on a Tabletopcited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Hume: Introducing System-2 Thinking in Visual-Language-Action Modelcited by 56
  2. 2025Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stackingcited by 0
  3. 2025MO-SeGMan: Rearrangement Planning Framework for Multi Objective Sequential and Guided Manipulation in Constrained Environmentscited by 0