Explore the cutting-edge research on improving physics simulation for AI applications in this 59-minute Stanford University seminar. Delve into Professor Karen Liu's work on overcoming the sim-to-reality gap by enhancing physics engines rather than control policies. Learn about the development of "learnable" physics engines, efficient training techniques, and progress in sim-to-real transfer involving human interaction. Gain insights into topics such as torque limits, gradient computation, human-aware robust sensing, and challenges in nonlinear dynamics. Discover how this research impacts the safe learning of robots in physical human-robot interaction scenarios without risking real people.