5th Annual Rutgers Robotics Workshop 2025

Name: 5th Annual Rutgers Robotics Workshop 2025
Start: 2025-10-10T08:30:00+00:00
End: 2025-10-10T17:30:00+00:00

Details

Date:: October 10
Time:: 8:30 am - 5:30 pm
Categories:: RAD Collaboratory Events, Regional/National Events, Rutgers Events
Website:: https://robotics.rutgers.edu/#workshop

Faculty, Students and Industry Participants are invited to join us for this annual event as part of the NSF National Research Traineeship SOCRATES (Socially Cognizant Robotics for a Technology Enhanced Society) at Rutgers.

Event Details

Date: Friday, October 10, 2025
Location: Civic Square Building (33 Livingston Ave, New Brunswick, NJ)
Registration: Required (Free)
Registration Link: Register Here

This annual robotics workshop brings together faculty, students, and industry representatives featuring both internal and external speakers as well as a poster session for students to present their robotics research.

Workshop Agenda

8:30 – 9:00 Check-in

9:00 – 9:30 Introduction

9:30 – 10:30 Plenary Speaker, Dhruv Shah, Google DeepMind, UC Berkeley

10:30 – 10:45 Poster Spotlight Talks

10:45 – 11:45 Coffee break & Poster Session

11:45 – 12:30 SOCRATES Faculty Lightning Talks: Socially Cognizant Robotics in Action

12:30 – 1:30 Networking Lunch

1:30 – 2:30 Industry/Alumni Lightning Talks: AI in industry

2:30 – 3:15 Student Talks (NRT fellows/trainees)

3:15 – 4:15 Afternoon Keynote

4:30 – 5:30 Robot Demonstrations and Industry Consortium Discussion

Featured Speaker

Dhruv Shah, PhD

Google DeepMind & Incoming Assistant Professor at Princeton University

“Evaluating and Improving Steerability of Generalist Robot Policies”

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.

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.