#279: Safe Robot Learning on Hardware, with Jaime Fernández Fisac Feb. 4, 2019

from Robohub Podcast· ·

Jaime Fernández Fisac on learning how to control robots while keeping them from hurting themselves or others.

In this interview, Audrow Nash interviews Jaime Fernández Fisac, a PhD student at University of California, Berkeley, working with Professors Shankar Sastry, Claire Tomlin, and Anca Dragan. Fisac is interested in ensuring that autonomous systems such as self-driving cars, delivery drones, and home robots can operate and learn in the world—while satisfying safety constraints. Towards this goal, Fisac discusses different examples of his work with unmanned aerial vehicles and talks about safe robot learning in general; including, the curse of dimensionality and how it impacts control problems (including how some systems can be decomposed into simpler control problems), how simulation can be leveraged before trying learning on a physical robot, safe sets, and how a robot can modify its behavior based on how confident it is that its model is correct.