ReloPush-BOSS: Optimization-guided Nonmonotone Rearrangement Planning for a Car-like Robot Pusher
Jeeho Ahn, Christoforos Mavrogiannis

TL;DR
ReloPush-BOSS is a novel rearrangement planning method for car-like robots that uses optimization-guided prerelocations and a traversability graph to efficiently solve complex cluttered environment tasks.
Contribution
It introduces a new optimization-guided prerelocation strategy combined with a traversability graph for improved rearrangement planning in dense clutter.
Findings
Achieves higher success rates than state-of-the-art methods.
Finds shorter and more feasible pushing paths.
Demonstrates robustness in hardware experiments.
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Robotic Path Planning Algorithms · Modular Robots and Swarm Intelligence
