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

arXiv: https://arxiv.org/abs/2309.13707 · HTML: https://mlsanity.com/paper/2309.13707/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 32 references that are in the map.
- First posted on arXiv 24 Sep 2023.

## Builds on (most cited first, 22 of 22)

- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [Task and Motion Planning with Large Language Models for Object Rearrangement](https://mlsanity.com/paper/2303.06247/) (2023)
- [Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning](https://mlsanity.com/paper/1904.10348/) (2019)
- [Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation](https://mlsanity.com/paper/2003.10863/) (2020)
- [Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement](https://mlsanity.com/paper/2105.02857/) (2021)
- [Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting](https://mlsanity.com/paper/1912.07024/) (2019)
- [Active Learning of Abstract Plan Feasibility](https://mlsanity.com/paper/2107.00683/) (2021)
- [Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps](https://mlsanity.com/paper/1711.07369/) (2017)
- [Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search](https://mlsanity.com/paper/2101.12241/) (2021)
- [Fast High-Quality Tabletop Rearrangement in Bounded Workspace](https://mlsanity.com/paper/2110.12325/) (2021)
- [Persistent Homology for Effective Non-Prehensile Manipulation](https://mlsanity.com/paper/2202.02937/) (2022)
- [DIPN: Deep Interaction Prediction Network with Application to Clutter Removal](https://mlsanity.com/paper/2011.04692/) (2020)
- [Visually Grounded Task and Motion Planning for Mobile Manipulation](https://mlsanity.com/paper/2202.10667/) (2022)
- [Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement](https://mlsanity.com/paper/2207.08078/) (2022)
- [Minimizing Running Buffers for Tabletop Object Rearrangement: Complexity, Fast Algorithms, and Applications](https://mlsanity.com/paper/2304.01764/) (2023)
- [Scalable and Probabilistically Complete Planning for Robotic Spatial Extrusion](https://mlsanity.com/paper/2002.02360/) (2020)
- [Object Reconfiguration with Simulation-Derived Feasible Actions](https://mlsanity.com/paper/2302.14161/) (2023)
- [Assembly Sequence Planning for Motion Planning](https://mlsanity.com/paper/1609.03108/) (2016)
- [Lazy Rearrangement Planning in Confined Spaces](https://mlsanity.com/paper/2203.10379/) (2022)
- [Effectively Rearranging Heterogeneous Objects on Cluttered Tabletops](https://mlsanity.com/paper/2306.14240/) (2023)
- [Optimal and Stable Multi-Layer Object Rearrangement on a Tabletop](https://mlsanity.com/paper/2306.14251/) (2023)

## Built on by (most cited first, 3 of 3)

- [Hume: Introducing System-2 Thinking in Visual-Language-Action Model](https://mlsanity.com/paper/2505.21432/) (2025)
- [Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking](https://mlsanity.com/paper/2509.22828/) (2025)
- [MO-SeGMan: Rearrangement Planning Framework for Multi Objective Sequential and Guided Manipulation in Constrained Environments](https://mlsanity.com/paper/2511.01476/) (2025)

## 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/