Robot pick and place systems have traditionally decoupled grasp, placement, and motion planning to build sequential optimization pipelines with the assumption that the individual components will be able to work together. However, this separation introduces sub-optimality, as grasp choices may limit or even prohibit feasible motions for a robot to reach the target placement pose, particularly in cluttered environments with narrow passages. To this end, we propose a forest-based planning framework to simultaneously find grasp configurations and feasible robot motions that explicitly satisfy downstream placement configurations paired with the selected grasps. Our proposed framework leverages a bidirectional sampling-based approach to build a start forest, rooted at the feasible grasp regions, and a goal forest, rooted at the feasible placement regions, to facilitate the search through randomly explored motions that connect valid pairs of grasp and placement trees. We demonstrate that the framework's inherent parallelism enables superlinear speedup, making it scalable for applications for redundant robot arms (e.g., 7 Degrees of Freedom) to work efficiently in highly cluttered environments. Extensive experiments in simulation demonstrate the robustness and efficiency of the proposed framework in comparison with multiple baselines under diverse scenarios.
View on arXiv@article{leebron2025_2504.04598,
title={ B4P: Simultaneous Grasp and Motion Planning for Object Placement via Parallelized Bidirectional Forests and Path Repair },
author={ Benjamin H. Leebron and Kejia Ren and Yiting Chen and Kaiyu Hang },
journal={arXiv preprint arXiv:2504.04598},
year={ 2025 }
}