UC San Diego

Motion planning is one of the most critical tasks in robotics, as it is one of the few critical functions for robot autonomy. This component requires fast computations and generalization to different environments for problems such as collision avoidance. Deep learning, as a new fast-growing field, offers great advances in computational speed and generalization. It has shown success in computer vision and reinforcement learning, which is closely related to motion planning. In this thesis, we will investigate the combination of learning and motion planning methods. Specifically, we separately consider individual components of motion planning tasks. By combining the proposed learning-based methods for each component, we can obtain an integrated end-to-end learning-based motion planning algorithm. We show experimental results for each component. In general, our learning-based methods showed high computational speed with generalization in several motion planning tasks.