Episode 8 (English) - Safety-Validation of autonomous systems (using Machine Learning) - with Anthony Corso

Anthony Corso is a Ph.D. student in the Aeronautics and
Astronautics Department at Stanford University where he is advised
by Professor Mykel Kochenderfer in the Stanford Intelligent Systems
Laboratory (SISL). He studies approaches for the validation of
safety-critical autonomous systems with an emphasis on
interpretability and scalability. In this podcast he talked about
safety-validation of autonomous systems. The latter includes
systems such as robots, cars, aircraft, and planetary rovers
equally. In May he published a paper which deals with different
algorithms for black-box safety validation. One of the approaches
is to use reinforcement learning, which was discussed in the
podcast in more detail. He also briefly introduced the
Next-Generation Airborne Collision Avoidance System ACAS X, in
which development Professor Kochenderfer was heavily involved. ACAS
X takes advantage of Dynamic Programming, an algorithm for optimal
decision making. The mentioned papers, further readings and an
interesting podcast can be found here: The paper mentioned above: A
Survey of Algorithms for Black-Box Safety Validation The paper on
Adaptive Stress Testing (AST): Adaptive stress testing with reward
augmentation for autonomous vehicle validation The AST toolbox
mentioned in the podcast: AST-Toolbox The CARLA simulator mentioned
in the podcast: CARLA A paper on ACAS X: Next-Generation Airborne
Collision Avoidance System An episode of Standford University's
podcast The Future of Everything with Mykel Kochenderfer where he
talkes about ACAS X and Artificial Intelligence (AI) in
safety-critical systems: Mykel Kochenderfer: AI and safety-critical
systems