Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute Seminar Series

Speaker: Anirudha Majumdar, Mechanical & Aerospace Engineering, Princeton University

Talk Title: Safety and Generalization for Learning-Based Robot Control

Series: Center for Cyber-Physical Systems and Internet of Things

Abstract: The ability of machine learning techniques to process rich sensory inputs such as vision makes them highly appealing for use in robotic systems (e.g., micro aerial vehicles and robotic manipulators). However, the increasing adoption of learning-based components in the robotics perception and control pipeline poses an important challenge: how can we guarantee the safety and performance of such systems? As an example, consider a micro aerial vehicle that learns to navigate using a thousand different obstacle environments or a robotic manipulator that learns to grasp using a million objects in a dataset. How likely are these systems to remain safe and perform well on a novel (i.e., previously unseen) environment or object? How can we learn control policies for robotic systems that provably generalize to environments that our robot has not previously encountered? Unfortunately, existing approaches either do not provide such guarantees or do so only under very restrictive assumptions.

In this talk, I will present our group's work on developing a principled theoretical and algorithmic framework for learning control policies for robotic systems with formal guarantees on generalization to novel environments. The key technical insight is to leverage and extend powerful techniques from generalization theory in theoretical machine learning. We apply our techniques on problems including vision-based navigation and grasping in order to demonstrate the ability to provide strong generalization guarantees on robotic systems with complicated (e.g., nonlinear/hybrid) dynamics, rich sensory inputs (e.g., RGB-D), and neural network-based control policies.

Biography: Anirudha Majumdar is an Assistant Professor at Princeton University in the Mechanical and Aerospace Engineering (MAE) department and Associated Faculty in the Computer Science department. He also holds a part-time position as a Visiting Research Scientist at the Google AI Lab in Princeton. His group works on controlling highly agile robotic systems in a manner that allows us to make formal guarantees on their safety and performance.

Majumdar received a Ph.D. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 2016, and a B.S.E. in Mechanical Engineering and Mathematics from the University of Pennsylvania in 2011. Subsequently, he was a postdoctoral scholar at Stanford University from 2016 to 2017 at the Autonomous Systems Lab in the Aeronautics and Astronautics department. He is a recipient of the ONR YIP award, the NSF CAREER award, the Google Faculty Research Award (twice), the Amazon Research Award (twice), the Young Faculty Researcher Award from the Toyota Research Institute, the Best Conference Paper Award at the International Conference on Robotics and Automation (ICRA), the Paper of the Year Award from the International Journal of Robotics Research (IJRR), the Alfred Rheinstein Faculty Award (Princeton University), and the Excellence in Teaching Teaching Award from Princeton's School of Engineering and Applied Science.


Host: Somil Bansal, somilban@usc.edu

Webcast: https://usc.zoom.us/j/92742577270?pwd=bEpXaWJudjZWRksyNk5lL1owUUdBQT09

Location: Online

WebCast Link: https://usc.zoom.us/j/92742577270?pwd=bEpXaWJudjZWRksyNk5lL1owUUdBQT09

Audiences: Everyone Is Invited

Contact: Talyia White