Learning Without Simulations? UC Berkeley’s DayDreamer Establishes a Strong Baseline for Real-World Robotic Training | Synced

In the new paper DayDreamer: World Models for Physical Robot Learning, researchers from the University of California, Berkeley leverage recent advances in the Dreamer world model to enable online reinforcement learning for robot training without simulators or demonstrations, establishing a strong baseline for efficient real-world robotic learning.