Dhruv Shah

I am interested in building intelligent and useful robotic systems that can be deployed reliably in challenging environments. My 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. We take 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. We draw inspiration from cognitive science and psychology to build physical AI systems at the interface of perception, learning, and control.

1. ViNT: A Foundation Model for Visual Navigation. D. Shah et al.. Annual Conference on Robot Learning (CoRL) 2023. 2. Learning Robotic Navigation from Experience: Principles, Methods, and Recent Results. S. Levine and D. Shah. Philosophical Transactions of the Royal Society B, 2022. 3. Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning. D. Shah et al. International Conference on Learning Representations (ICLR) 2022. 4. LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action. D. Shah et al. Annual Conference on Robot Learning (CoRL) 2022. 5. NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration. A. Sridhar, D. Shah, et al. IEEE International Conference on Robotics and Automation (ICRA) 2024.