Acquiring Data for Learning: Modern Approaches and Challenges

This lecture introduces data-driven learning for optimal controller acquisition, focusing on modern approaches and challenges. It covers programming by demonstration, data-driven learning, reinforcement learning, inverse reinforcement learning, and motion representations. The instructor discusses teleoperation, kinesthetic teaching, observational learning, and various teleoperation interfaces. The lecture explores the correspondence problem, data sensitivity, and task variability in learning from demonstration. It also delves into modeling hitting tasks using dynamical systems-based control, teaching compliant control, and considerations for learning human skills through demonstration.