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#279: Safe Robot Learning on Hardware, with Jaime Fernández Fisac



In this interview, Audrow Nash interviews Jaime Fernández Fisac, a PhD student at University of California, Berkeley, in Anca Dragan’s InterACT Lab. 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

Mouser Electronics – TE Connectivity HDC Dynamic Module

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SICK is taking safety to the next level with collaborative robot systems

There is an increasing demand for collaborative robots that can work autonomously and adapt to changing production conditions. This requires reliable sensors that detect human presence and can overcome future challenges with the development of collaborative technologies.

A new machine learning based intention detection method using first-person-view camera for Exo Glove Poly II

A Korean research team has proposed a new paradigm for a wearable hand robot that can aid people with lost hand mobility. The hand robot collects user behaviors with a machine learning algorithm to determine the user's intention.

ASU’s Southwest Robotics Symposium previews the new technology guiding the next wave of human-robot interaction

“We can rely on the brain of the human and the muscles, eyes and sense of touch of the robot in places where humans cannot, or should not, be,” said Khatib. “For example, we will be able to safely repair underwater structures
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