Explore the cutting-edge developments in autonomous robot learning and planning in this 46-minute conference talk by Glen Berseth, assistant professor at the Université de Montréal and co-director of the Robotics and Embodied AI Lab. Delve into the challenges of creating robotic agents capable of solving complex tasks and scientific problems. Learn about key concepts such as Markov Decision Processes, deep reinforcement learning, task decomposition, and lifelong learning. Discover innovative approaches like hierarchical controllers, curriculum training, and intrinsic rewards. Gain insights into representation learning for complex observations and future goals in robotics research. Understand how these advancements aim to bridge the gap between human and robotic problem-solving capabilities across various domains.