Learning-Guided Exploration for Efficient Sampling-Based Motion Planning in High Dimensions

Abstract

Optimal motion planning is a long-studied problem with a wide range of applications in robotics, from grasping to navigation. While sampling-based motion planning methods have made solving such problems significantly more feasible, these methods still often struggle in high-dimensional spaces wherein exploration is computationally costly. In this paper, we propose a new motion planning algorithm that reduces the computational burden of the exploration process. The proposed algorithm utilizes a guidance policy acquired offline through model-free reinforcement learning. The guidance policy is used to bias the exploration process in motion planning and to guide it toward promising regions of the state space. Moreover, we show that the gradients of the corresponding learned value function can be used to locally fine-tune the sampled states. We empirically demonstrate that the proposed approach can significantly reduce planning time and improve success rate and path quality.