Evaluating and Improving Steerability of Generalist Robot Policies
Date: April 18, 2025 @ 3:00-4:00PM | Location: Gates B03 | Speaker: Dhruv Shah | Affiliation: Google Deepmind/Princeton
Abstract:
General-purpose robot policies hold immense promise, yet they often struggle to generalize to novel scenarios, particularly struggling with grounding language in the physical world. In this talk, I will first propose a systematic taxonomy of robot generalization, providing a framework for understanding and evaluating current state-of-the-art generalist policies. This taxonomy highlights key limitations and areas for improvement. I will then discuss a simple idea for improving the steerability of these policies by improving language grounding in robotic manipulation and navigation. Finally, I will present our recent effort in applying these principles to scaling up generalist policy learning for dexterous manipulation.
Bio:
Dhruv Shah is a Senior Research Scientist at Google DeepMind and an incoming Assistant Professor at Princeton University. He recently obtained his PhD in EECS at UC Berkeley, where he was 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 has been nominated for and won several Best Paper Awards at premier robotics conferences like RSS and ICRA. His work has also been featured in several media outlets, including IEEE Spectrum, TechXplore, and ZDNet, along with several international venues.