Robohub.org
ep.

279

podcast
 

Safe Robot Learning on Hardware with Jaime Fernández Fisac


by
04 February 2019



share this:




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.

Below are two videos of work that was discussed during the interview.  The top video is on a framework for learning-based control, and the bottom video discusses adjusting the robot’s confidence about a human’s actions based on how predictably the human is behaving.

Jaime Fernández Fisac

Jaime Fernández Fisac is a final-year Ph.D. candidate in Electrical Engineering and Computer Sciences at the University of California, Berkeley. He received a B.S./M.S. degree in Electrical Engineering from the Universidad Politécnica de Madrid, Spain, in 2012, and a M.Sc. in Aeronautics from Cranfield University, U.K., in 2013. He is a recipient of the La Caixa Foundation fellowship. His research interests lie between control theory and artificial intelligence, with a focus on safety assurance for autonomous systems. He works to enable AI systems to reason explicitly about the gap between their models and the real world, so that they can safely interact with uncertain environments and human beings, even under inaccurate assumptions.

Links



tags: , , , , ,


Audrow Nash is a Software Engineer at Open Robotics and the host of the Sense Think Act Podcast
Audrow Nash is a Software Engineer at Open Robotics and the host of the Sense Think Act Podcast





Related posts :

How can robots acquire skills through interactions with the physical world? An interview with Jiaheng Hu

and   12 Feb 2026
Find out more about work published at the Conference on Robot Learning (CoRL).

Sven Koenig wins the 2026 ACM/SIGAI Autonomous Agents Research Award

  10 Feb 2026
Sven honoured for his work on AI planning and search.

Robot Talk Episode 143 – Robots for children, with Elmira Yadollahi

  06 Feb 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Elmira Yadollahi from Lancaster University about how children interact with and relate to robots.

New frontiers in robotics at CES 2026

  03 Feb 2026
Henry Hickson reports on the exciting developments in robotics at Consumer Electronics Show 2026.

Robot Talk Episode 142 – Collaborative robot arms, with Mark Gray

  30 Jan 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Mark Gray from Universal Robots about their lightweight robotic arms that work alongside humans.

Robot Talk Episode 141 – Our relationship with robot swarms, with Razanne Abu-Aisheh

  23 Jan 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Razanne Abu-Aisheh from the University of Bristol about how people feel about interacting with robot swarms.

Vine-inspired robotic gripper gently lifts heavy and fragile objects

  23 Jan 2026
The new design could be adapted to assist the elderly, sort warehouse products, or unload heavy cargo.

Robot Talk Episode 140 – Robot balance and agility, with Amir Patel

  16 Jan 2026
In the latest episode of the Robot Talk podcast, Claire chatted to Amir Patel from University College London about designing robots with the agility and manoeuvrability of a cheetah.


Robohub is supported by:





 













©2026.01 - Association for the Understanding of Artificial Intelligence