Bike Zhang
Bike Zhang is a PhD student at UC Berkeley, advised by Professor Koushil Sreenath. His research focuses on the intersection between control theory and machine learning, with the aim of endowing robots with greater agility, safety, and intelligence.
Abstract
In this talk, I will introduce a sim-to-real learning-based approach for real-world humanoid locomotion. Our controller is a causal Transformer trained by autoregressive prediction of future actions from the history of observations and actions. We hypothesize that the observation-action history contains useful information about the world that a powerful Transformer model can use to adapt its behavior in-context, without updating its weights. This policy is trained with large-scale model-free reinforcement learning on an ensemble of randomized environments in simulation and deployed to the real world in a zero-shot fashion. The learned controller is evaluated in high-fidelity simulation and successfully deployed to a real Digit humanoid robot.