Classical Motion Planning

Classical motion planning aims to find optimal and collision-free paths for robots navigating complex environments, a crucial problem in robotics. Current research focuses on improving the robustness and efficiency of these planners, particularly through the integration of machine learning techniques like reinforcement learning and neural networks to handle dynamic environments and diverse robot morphologies. This includes developing novel architectures such as diffusion models guided by ensemble cost functions and leveraging classical planners as safety modules within learning frameworks. These advancements are vital for enabling more adaptable and reliable autonomous systems in various applications, from industrial automation to personal robotics.