Humans plan and solve many tasks with ease. They can grow to perform incredible gymnastics, prove that black holes exist, and produce works of art, all starting from the same base learning system. While learning methods such as deep reinforcement learning have shown progress in simulated planning and control problems, they struggle to produce the same diverse, intelligent behaviour, especially in systems that interact in the real world (robots). This talk aims to discuss these limitations, provide methods to overcome them and enable agents capable of training autonomously to become learning and adapting systems that require little supervision while performing diverse tasks. The talk will present a series of works covering new, more robust Sim2Real methods, offline RL methods for longer planning tasks, and advances in generalization in planning to enable the creation of a single large policy to control all types of robots across diverse tasks.
Glen Berseth is an assistant professor at the Université de Montréal, a core academic member of the Mila - Quebec AI Institute, Canada CIFAR AI chair, and co-director of theRobotics and Embodied AI Lab (REAL). He was a Postdoctoral Researcher at Berkeley Artificial Intelligence Research (BAIR), working with Sergey Levine. His current research focuses on machine learning and solving real-world sequential decision-making problems (planning/RL), such as robotics, scientific discovery and adaptive clean technology. The specifics of his research have covered the areas of human-robot collaboration, generalization, reinforcement learning, continual learning, meta-learning, multi-agent learning, and hierarchical learning. Dr. Berseth has published across the top venues in robotics, machine learning, and computer animation in his work. He also teaches courses on data science and robot learning at Université de Montréal and Mila, covering the most recent research on machine learning techniques for creating generalist agents. He has also co-created a new conference for reinforcement learning research.