Dhruv Shah
Dhruv Shah is interested in building intelligent and useful robotic systems that can be deployed reliably in challenging environments. His research group broadly focuses on the intersection of machine learning and robotics, with an emphasis on large-scale robot learning (foundation models of & for robotics), out-of-distribution generalization, reinforcement learning, long-horizon reasoning and planning, representation learning from high-dimensional observations, modeling multi-agent and human-robot interactions, and continual learning. He takes a full-stack approach to robotics by focusing on all aspects of the problem, ranging from algorithmic innovation to system design, and value both the analytical and empirical nature of science. He draws inspiration from cognitive science and psychology to build physical AI systems at the interface of perception, learning, and control.