Learning-Guided Exploration for Efficient Sampling-Based Motion Planning in High Dimensions
Liam Schramm,Abdeslam Boularias,Liam Schramm,Abdeslam Boularias
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 t...
