Abstract

ABSTRACT Data-efficiency is useful in robotic learning, where real-world data can be expensive and time-consuming to acquire. Probabilistic dynamics models can help accelerate learning by mitigating overfitting and providing richer supervision signals than model-free control methods. An additional benefit of probabilistic models is their ability to detect out-of-distribution events, useful in certain safety-critical settings where control should not deviate from demonstration data. This talk investigates how deep probabilistic models can benefit learning safe control fast, when either learning from scratch, or from imitation data.

SPEAKER BIO Rowan McAllister is a research scientist at Toyota Research Institute. His research is concerned with probabilistic modelling for data-efficient learning of control, often with autonomous vehicle applications in mind. Rowan received a PhD from Cambridge in 2017 and was a postdoctoral scholar at UC Berkeley until 2020.