Abstract: Recent breathtaking advances in AI and robotics have brought us closer to building general-purpose robots in the real world, e.g., humanoids capable of performing a wide range of human tasks in complex environments. Two key challenges in realizing such general-purpose robots are: (1) achieving “breadth” in task/environment diversity, i.e., the generalist aspect, and (2) achieving “depth” in task execution, i.e., the agility aspect. In this talk, I will present recent works that aim to achieve both generalist-level adaptability and specialist-level agility, demonstrated across various real-world robots, including full-size humanoids, quadrupeds, aerial robots, and ground vehicles. The first part of the talk focuses on learning agile and general-purpose humanoid whole-body control using sim2real reinforcement learning. The second part will discuss the limitations of such end2end sim2real pipelines and how combining learning with control can enhance safety, efficiency, and adaptability.
Bio: Guanya Shi is an Assistant Professor at the Robotics Institute at Carnegie Mellon University, leading the Learning and Control for Agile Robotics Lab. He completed his Ph.D. in Control and Dynamical Systems in 2022 from Caltech. Before joining CMU, he was a postdoctoral scholar at the University of Washington. He is broadly interested in the intersection of machine learning and control, spanning the entire spectrum from theory and foundation, algorithm design, to real-world agile robotics. Guanya was the recipient of several awards, including the Simoudis Discovery Prize and the Ben P.C. Chou Doctoral Prize from Caltech, the Rising Star in Data Science, and the Outstanding Student Paper Award Finalist at RSS. Guanya is an Associate Editor of IEEE Robotics and Automation Letters.
