TALK    [MERL Seminar Series 2024] Stefanos Nikolaidis presents talk titled Enhancing the Efficiency and Robustness of Human-Robot Interactions

Date & Time:

Friday, March 8, 2024; 1:00 PM

Abstract:

While robots have been successfully deployed in factory floors and warehouses, there has been limited progress in having them perform physical tasks with people at home and in the workplace. I aim to bridge the gap between their current performance in human environments and what robots are capable of doing, by making human-robot interactions efficient and robust.

In the first part of my talk, I discuss enhancing the efficiency of human-robot interactions by enabling robot manipulators to infer the preference of a human teammate and proactively assist them in a collaborative task. I show how we can leverage similarities between different users and tasks to learn compact representations of user preferences and use these representations as priors for efficient inference.

In the second part, I talk about enhancing the robustness of human-robot interactions by algorithmically generating diverse and realistic scenarios in simulation that reveal system failures. I propose formulating the problem of algorithmic scenario generation as a quality diversity problem and show how standard quality diversity algorithms can discover surprising and unexpected failure cases. I then discuss the development of a new class of quality diversity algorithms that significantly improve the search of the scenario space and the integration of these algorithms with generative models, which enables the generation of complex and realistic scenarios.

Finally, I conclude the talk with applications in mining operations, collaborative manufacturing and assistive care.

Speaker:

Stefanos Nikolaidis
University of Southern California

Stefanos Nikolaidis is an Assistant Professor of Computer Science and the Fluor Early Career Chair in Engineering at the University of Southern California, where he leads the Interactive and Collaborative Autonomous Robotics Systems (ICAROS) lab. His research lies in the intersection of human-robot interaction, algorithmic scenario generation, quality diversity optimization, and machine learning, and leads to end-to-end solutions that enable deployed robotic systems to act robustly when interacting with people in practical, real-world applications. Stefanos completed his PhD at Carnegie Mellon's Robotics Institute and received an MS from MIT, a MEng from the University of Tokyo and a BS from the National Technical University of Athens. Stefanos was the sole recipient of the 2022 Agilent Early Career Professor Award for his work on human-robot collaboration, as well as the recipient of an NSF CAREER award for his work on “Enhancing the Robustness of Human-Robot Interactions via Automatic Scenario Generation.”

MERL Host:

Siddarth Jain

Research Areas:

Machine Learning, Robotics, Human-Computer Interaction