Safe Learning in Robotics
Explore the Richard M. Karp Distinguished Lecture on safe learning in robotics, delivered by Claire Tomlin from UC Berkeley. Delve into crucial topics such as air traffic control, UAV applications, and operations in unstructured environments. Discover reachable set propagation, collision avoidance techniques, and strategies for overcoming the curse of dimensionality. Learn about fast and safe planning methods, including precomputed tracking bounds and RRT applications. Examine the application of these concepts to noisily rational human models, Bayesian model confidence, and multi-agent scenarios. Investigate the use of machine learning for computing sets, safe policy gradient reinforcement learning, and online disturbance model validation. Gain insights into cutting-edge approaches for maintaining safety while learning in robotic systems.