Safe Learning for Robotics

This lecture by the instructor covers topics related to safe and data-efficient visual learning for robotics, focusing on control theory, learning dynamics models, environment uncertainty, and navigation in unknown environments. The lecture discusses challenges in AI and learning for autonomy, perception systems, end-to-end learning, and expert policy using optimal control. It also explores the integration of vision with model-based control, success rates in reaching goals, and comparisons between different mapping approaches. Key takeaways include the importance of optimal control, subgoal representation, and handling image distortions for generalization.