Jaime Fernandez Fisac

Jaime is currently an Assistant Professor in Electrical Engineering at Princeton University. Prior to this appointment, he worked at Waymo as a Research Scientist

While Jaime was at CHAI as a PhD student, he worked on autonomous robots in both academia and industry, with a
particular focus on collision avoidance and multi-agent systems. Broadly, his research focused on safely introducing robotics into society.

Under the guidance of CHAI Professors Anca Dragan and Tom Griffiths, and along with fellow CHAI graduate students Vael
Gates and Dylan Hadfield-Menell, Jaime worked on solving the cooperative inverse reinforcement learning (CIRL) dynamic
game using well-established models of human inference, decision making, and theory of mind from the cognitive science
literature. Previous solutions have relied on modelling both the human and robot as perfectly rational and able to
coordinate in advance, which are nontrivial assumptions in the real world. Instead, Jaime and his colleagues’ work
models the human as pedagogic (i.e., her behaviour will aim to be instructive) and the robot as pragmatic (i.e., it
knows the human is not perfectly rational but is still trying to teach it). Results suggest this formulation produces
robots that are more competent collaborators (paper). In the past, Jaime has
also researched how we can have robots choose their course of action in a way that will be easy for a human observer to
anticipate (paper) and how incorporating
uncertainty into a safety framework for robotic systems that works in conjunction with their learning process can
provide meaningful safety guarantees (paper).

At Princeton, Jaime intends to look into how AI systems can utilize models of human cognition and behavior to ensure
safe interaction with people. He believes that the safer robots will be those that engage their users and procure their
cooperation, rather than try to protect against their indifference. He hopes that designing safe human-centered robotic
systems in the short term will give us key insights to tackle the broader, long-term AI safety problem. Also, he has
done research on having AI treat their models as fallible to protect against overconfidence. You can explore more about
this topic by watching this video.

You can learn more about Jaime at his website.