Dreamer

The talk explains sample efficiency and presents world models as a way for AI systems to learn and plan with fewer real-world examples. It contrasts differentiable control, where known physics enables optimization, with stochastic settings such as games, driving, and robotics, where other agents and large action spaces make reinforcement learning and test-time planning costly. It surveys model-based RL, synthetic rollouts, video-based world models, and JEPA’s prediction of future states in latent space. The speakers also discuss open problems including inaccurate physics, out-of-distribution adaptation, slow planning, and the scarcity of action and tactile data for robots.