The next D5 Seminar, “Deep Learning, Optimal Control, and Bio-Inspired Control for Dynamic Robots” will be held by Guillaume Bellegarda (EPFL) on Friday, December 6 at 1:30 p.m. in room A008.
Recent advances in machine learning, control, and robotics show promising results towards integrating autonomous systems into society. Legged robots in particular suggest potential for the same dynamic capabilities as humans to adapt to everyday, and even challenging, environments. However, when compared with humans and animals, state-of-the-art robotic systems do not yet demonstrate the same agility nor intelligence to navigate the real world. While important contributions have been made to approach human levels in specific tasks, the desired system generalizability to interact with and adapt to new environments remains challenging. Additionally, for tasks learned with machine learning in which robotic systems do approach or surmount human-level skills, the underlying neural network function approximation lacks interpretability and performance guarantees. This is true for both Artificial Neural Networks, as well as their biological counterparts that exist in animals. In this talk, we present several methods to maximize robotic system performance and explainability by leveraging ideas from machine learning, model-based control, and neuroscience. Example applications will be shown for highly dynamic motions on systems such as quadrupeds, vehicles, and wheel-legged robots.
