Stanford CS224R Deep Reinforcement Learning | Spring 2025 | Lecture 17: Advancing Robot Intelligence

The podcast discusses advancing robot intelligence using reinforcement learning, contrasting it with imitation learning, and highlighting the achievements of reinforcement learning in game playing, reasoning for large language models, and robotics. It emphasizes the importance of well-specified rewards and the ability to run policies at scale for successful on-policy reinforcement learning. The discussion covers sim-to-real challenges in robotics, presenting real-world results of robots navigating various terrains using a policy trained in simulation and adapted to real-world conditions through online estimation of environmental parameters. The podcast further explores adding vision to robot walking, questioning the necessity of terrain maps, and proposes directly coupling vision and control in the system, also touching on applications in dexterous manipulation and drone flight, and concludes with a discussion on the future of robotics, emphasizing the need for better simulation technology and general reward models.