Event
Microsoft Future Leaders in Robotics and AI Seminar Series: Dhruv Shah
Friday, April 5, 2024
2:30 p.m.
Online Seminar
The Foundation Model Path to Open-World Robots
Dhruv Shah
PhD Candidate
University of California Berkeley
Abstract
Robot learning methods typically rely either on learning from large-scale simulation modeling and transferring to real-world settings or by collecting real-world interaction data on the target robot. While this paradigm has been successful for solving simple tasks in structured environments, it may fall short for tasks that are hard to simulate accurately (e.g. in off-road racing) and where data collection may be expensive (e.g. micro UAVs with
Bio
Dhruv Shah is a final year PhD candidate in EECS at UC Berkeley, where he is advised by Sergey Levine. His research spans the fields of machine learning and robotics, with the goal of building autonomous robots that can combine large-scale learning with real-world deployment. Dhruv is a Microsoft Future Leader in Robotics & AI (2024), Berkeley Fellow, and his research was a finalist for the Best Systems Paper Award at RSS 2022. His work has also been featured in several media outlets, including IEEE Spectrum, TechXplore, Two Minute Papers, and ZDNet, along with several international venues.
