Embodied Artificial Intelligence Safety

Catalog description

Safety is a nuanced concept. For embodied systems, like robots, we commonly equate safety with collision-avoidance. But out in the "open world" it can be much more: for example, a safe mobile manipulator should understand when it is not confident about a requested task and understand that areas roped off by caution tape should never be breached. In this class, we study the question of if (and how) the rise of modern artificial intelligence (AI) models (e.g., deep neural trajectory predictors, large vision-language models, and latent world models) can be harnessed to unlock new avenues for generalizing safety to the open world. From a foundations perspective, we study safety methods from two complementary communities: control theory (which enables the computation of safe decisions) and machine learning (which enables uncertainty quantification and anomaly detection). Throughout the class, there will also be several guest lectures from experts in the field. Students will practice essential research skills including reviewing papers, writing project proposals, and technical communication. Course Website: https://abajcsy.github.io/embodied-ai-safety/