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arXiv · 2503.04409

SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments

Cankut Bora Tuncer·Dilruba Sultan Haliloglu·Ozgur S. Oguz
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Abstract

In this paper, we present SeGMan, a hybrid motion planning framework that integrates sampling-based and optimization-based techniques with a guided forward search to address complex, constrained sequential manipulation challenges, such as pick-and-place puzzles. SeGMan incorporates an adaptive subgoal selection method that adjusts the granularity of subgoals, enhancing overall efficiency. Furthermore, proposed generalizable heuristics guide the forward search in a more targeted manner. Extensive evaluations in maze-like tasks populated with numerous objects and obstacles demonstrate that SeGMan is capable of generating not only consistent and computationally efficient manipulation plans but also outperform state-of-the-art approaches.

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Cankut Bora Tuncer, Dilruba Sultan Haliloglu, Ozgur S. Oguz. 2025-03-06. SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments. https://arxiv.org/abs/2503.04409

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