Feedback Motion Planning

Feedback motion planning aims to generate robot trajectories that are robust to uncertainties and disturbances, unlike traditional open-loop methods. Current research emphasizes integrating sampling-based planners (like RRT) with feedback control techniques, often leveraging deep reinforcement learning, neural networks, or Lyapunov-based methods to ensure stability and safety. This approach is crucial for navigating complex, dynamic environments and is being actively explored using various model architectures, including those based on large language models and contraction theory, to achieve real-time performance and safety guarantees. The resulting advancements have significant implications for autonomous navigation in robotics, particularly in unstructured or unpredictable settings.