Explore cutting-edge research on joint optimization of physical design and computational reasoning for intelligent agents in this hour-long lecture by Matthew Walter from Toyota Technological Institute at Chicago. Delve into innovative learning-based methods for automated, data-driven optimization of sensor networks and physical configurations alongside computational inference and control. Discover frameworks for optimizing sensor network design, legged robot structures, and soft robot designs coupled with control policies. Gain insights into improving test-time generalization of learned policies and the potential impact on various robotic applications, from underwater vehicles to self-driving cars. Examine the speaker's background in intelligent, perceptually aware robots and his contributions to the field of machine learning-based solutions for robust robotic interactions in unstructured environments.