Isaac Remy

PhD student @ UW A&A

Hey there! I am a first-year PhD student in the University of Washington's Department of Aeronautics and Astronautics, where I am fortunate to be advised by Prof. Karen Leung.

I am broadly interested in building intelligent robotic systems that can assist and operate around humans while exhibiting safe, explainable, and verifiable behavior. To this end, I enjoy working at the intersection of control theory, dynamical systems, and deep learning; ultimately, I want to see robots transition from the factory floor to the complex and unpredictable environments we inhabit.

Outside of research, I love being outdoors, whether it be hiking, climbing, biking or swimming. Indoors, I can be seen weightlifting, playing video games, or curled up with a book.

News

Jan 16, 2025	Our paper on learning responsibility allocations for multi-agent interactions has been accepted to the 2025 American Controls Conference! See you in Denver!

Selected Publications

ACC

Learning responsibility allocations for multi-agent interactions: A differentiable optimization approach with control barrier functions

I. Remy ^† , D. Fridovich-Keil , and K. Leung

2025 American Controls Conference. More Information can be found here.

Abs PDF

From autonomous driving to package delivery, ensuring safe yet efficient multi-agent interaction is challenging as the interaction dynamics are influenced by hard-to-model factors such as social norms and contextual cues. Understanding these influences can aid in the design and evaluation of socially- aware autonomous agents whose behaviors are aligned with human values. In this work, we seek to codify factors governing safe multi-agent interactions via the lens of responsibility, i.e., an agent’s willingness to deviate from their desired control to accommodate safe interaction with others. Specifically, we propose a data-driven modeling approach based on control barrier functions and differentiable optimization that efficiently learns agents’ responsibility allocation from data. We demonstrate on synthetic and real-world datasets that we can obtain an interpretable and quantitative understanding of how much agents adjust their behavior to ensure the safety of others given their current environment.