Learning-based Motion Planning for Robots: Analyzing learning-based motion planning techniques for generating collision-free and efficient trajectories for robotic systems

Abstract

Learning-based motion planning has emerged as a powerful approach for generating collision-free and efficient trajectories for robotic systems. This paper provides a comprehensive analysis of various learning-based motion planning techniques, including deep reinforcement learning, imitation learning, and learning from demonstrations. We discuss the advantages and challenges of these techniques, highlighting their applicability in different robotic domains. Additionally, we examine the integration of perception and control with learning-based motion planning to enhance the robustness and adaptability of robotic systems. Through a series of case studies and experiments, we demonstrate the effectiveness of these techniques in real-world scenarios. Overall, this paper aims to provide a comprehensive understanding of the current state-of-the-art in learning-based motion planning for robots.