B4P: Simultaneous Grasp and Motion Planning for Object Placement via Parallelized Bidirectional Forests and Path Repair

2025-04-06

AI Summary

Decoupling grasp selection from motion planning leads to suboptimal performance in cluttered environments for pick-and-place tasks. Method: This paper proposes a joint optimization framework that simultaneously computes feasible grasp configurations, collision-free motion trajectories, and target placement poses. It introduces a novel parallel bidirectional forest structure: a start forest rooted at feasible grasp regions and a goal forest rooted at feasible placement regions, integrated with a path-repair mechanism to ensure end-to-end consistency. The approach combines sampling-based bidirectional search, parallel forest expansion, 7-DOF robotic arm kinematic modeling, and precise collision checking. Contribution/Results: Evaluation in simulation demonstrates significant improvements in success rate and planning efficiency under highly cluttered and narrow-passage conditions. The method achieves superlinear speedup via parallelization and enables robust real-time planning.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

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.

Problem

• Simultaneous grasp and motion planning for object placement
• Addressing sub-optimality in decoupled robot pick and place systems
• Enabling efficient planning in cluttered environments with narrow passages

Innovation

• Parallelized bidirectional forests for simultaneous planning
• Path repair connects grasp and placement trees
• Superlinear speedup via inherent parallelism