Dynamic Feasibility

Dynamic feasibility, in robotics and related fields, focuses on ensuring that planned actions or trajectories are physically realizable and satisfy constraints, such as avoiding collisions or respecting kinematic limitations. Current research emphasizes developing methods that efficiently assess and guarantee feasibility, often employing reinforcement learning, normalizing flows for probabilistic feasibility estimation, and convex optimization techniques to handle complex constraints. These advancements are crucial for improving the robustness and reliability of autonomous systems in diverse applications, ranging from robotic assembly and manipulation to multi-robot coordination and legged locomotion.