SeGMan通过分步引导搜索，高效解决复杂2D操作难题。
SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments
- 结合采样与优化方法，动态调整子目标粒度。
- 在多障碍迷宫任务中实现稳定且高效的规划路径。
- 适合需要精确操作的机器人场景，如工业装配。
本文提出SeGMan，一种融合采样与优化技术的混合运动规划框架，采用引导式前向搜索应对复杂的、受约束的序列操作挑战，例如拾取-放置谜题。SeGMan引入自适应子目标选择机制，根据任务需求动态调整子目标粒度，提升整体效率。此外，提出的可泛化启发式策略使前向搜索更具针对性。在布满大量物体和障碍物的迷宫类任务中进行的广泛评估表明，SeGMan不仅能生成一致且计算高效的操纵计划，还优于现有最先进方法。
原文摘要 · Abstract (English)
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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