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

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.

@article{tuncer2025_2503.04409,
  title={ SeGMan: Sequential and Guided Manipulation Planner for Robust Planning in 2D Constrained Environments },
  author={ Cankut Bora Tuncer and Dilruba Sultan Haliloglu and Ozgur S. Oguz },
  journal={arXiv preprint arXiv:2503.04409},
  year={ 2025 }
}