ReloPush-BOSS: Optimization-guided Nonmonotone Rearrangement Planning for a Car-like Robot Pusher

2026-01-29

AI Summary

This work addresses the challenge of non-monotonic object rearrangement for car-like robots operating in dense, cluttered environments under kinematic, geometric, and physical constraints. The authors propose a pre-relocation optimization method guided by Dubins path classification, which constructs a multi-constraint-aware object traversability graph and integrates depth-first search to generate efficient rearrangement sequences that effectively avoid local minima. Experimental results demonstrate that, in highly congested scenes with up to 13 objects, the proposed approach significantly outperforms existing methods in both planning success rate and computational efficiency, consistently yielding the shortest pushing trajectories. The robustness of the method is further validated through hardware experiments using a 1:10 scale car-like robot platform.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Combinatorial Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We focus on multi-object rearrangement planning in densely cluttered environments using a car-like robot pusher. The combination of kinematic, geometric and physics constraints underlying this domain results in challenging nonmonotone problem instances which demand breaking each manipulation action into multiple parts to achieve a desired object rearrangement. Prior work tackles such instances by planning prerelocations, temporary object displacements that enable constraint satisfaction, but deciding where to prerelocate remains difficult due to local minima leading to infeasible or high-cost paths. Our key insight is that these minima can be avoided by steering a prerelocation optimization toward low-cost regions informed by Dubins path classification. These optimized prerelocations are integrated into an object traversability graph that encodes kinematic, geometric, and pushing constraints. Searching this graph in a depth-first fashion results in efficient, feasible rearrangement sequences. Across a series of densely cluttered scenarios with up to 13 objects, our framework, ReloPush-BOSS, exhibits consistently highest success rates and shortest pushing paths compared to state-of-the-art baselines. Hardware experiments on a 1/10 car-like pusher demonstrate the robustness of our approach. Code and footage from our experiments can be found at: https://fluentrobotics.com/relopushboss.

Problem

• rearrangement planning
• nonmonotone
• car-like robot
• prerelocation
• cluttered environments

Innovation

• nonmonotone rearrangement planning
• prerelocation optimization
• Dubins path classification
• object traversability graph
• car-like robot pushing