Dhruv Shah
Dhruv Shah is a robotics researcher whose work combines machine learning and foundation models for robot navigation and control. He holds a Ph.D. in electrical engineering and computer science from UC Berkeley, where Sergey Levine advised him, and a B.Tech. in electrical engineering from IIT Bombay. LM-Nav and ViNT brought pre-trained models to robotic navigation, while his 2024 Berkeley dissertation proposed foundation models for open-world robots. His goal-masked diffusion paper won an IEEE ICRA Best Conference Paper Award in 2024, and he coauthored the Gemini Robotics report for direct robot control. He previously co-led Gemini Robotics at Google DeepMind; as of October 2026, he is an assistant professor of robotics at Princeton and a research scientist at Google DeepMind.
- Now at
- Google DeepMind and Princeton University
Timeline
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2022
An RSS systems-paper finalist
In 2022, his research is a finalist for the Best Systems Paper Award at Robotics: Science and Systems.
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2023
LM-Nav and language instructions
At CoRL in 2023, he coauthors LM-Nav, which uses pre-trained navigation, image-language and language models to give users a high-level interface to robot navigation without fine-tuning or language-annotated robot data.
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2023
ViNT visual navigation
He coauthors ViNT in 2023, a Transformer-based foundation model for vision-based robotic navigation that adapts to downstream tasks.
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2024
A dissertation on open-world robots
He publishes his UC Berkeley dissertation, The Foundation Model Path to Open-World Robots, in 2024. It proposes general robot foundation models trained on data from different environments and embodiments, using egocentric vision.
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2024
A goal-masked diffusion award
In 2024, he coauthors Goal Masked Diffusion Policies for Unified Navigation and Exploration, which wins IEEE ICRA's Best Conference Paper Award.
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2024
Microsoft Future Leader
He is a Microsoft Future Leader in Robotics & AI in 2024.
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March 2025
Gemini Robotics for direct control
In March 2025, he coauthors the Gemini Robotics report, which introduces a family of Gemini 2.0-based models and a vision-language-action model for direct robot control.
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July 2026
Whole-body control in Gemini Robotics 2
In July 2026, he appears among the Gemini Robotics 2 team members credited for a model that controls humanoid robots with whole-body movement.
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As of October 2026
Princeton robotics faculty
His education includes a Ph.D. in EECS at UC Berkeley, where Sergey Levine advised him, and a B.Tech. in electrical engineering at IIT Bombay. He has spent time at Google Brain and Meta FAIR and previously co-led Gemini Robotics at Google DeepMind, scaling robotic intelligence for humanoids. As of October 2026, he is an assistant professor of robotics at Princeton, where he leads the PRISM group, and a research scientist at Google DeepMind.
Career
- NowGoogle DeepMindResearch Scientist
- NowPrinceton UniversityAssistant Professor of Robotics
Education
- 2024University of California, BerkeleyPh.D.
- IIT BombayB.Tech.
Sources and credits
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- Microsoft Future Leaders in Robotics and AI Seminar Series: Dhruv Shah
- LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
- ViNT: A Foundation Model for Visual Navigation
- The Foundation Model Path to Open-World Robots
- Awards and Finalists - 2024 IEEE International Conference on Robotics and Automation (ICRA 2024)
- Gemini Robotics: Bringing AI into the Physical World
- Gemini Robotics 2 brings whole body intelligence to robots
- Dhruv Shah
