Machine Learning for Robotics

Robots are typically far less capable in autonomous mode than in tele-operated mode. The few exceptions tend to stem from long days (and more often weeks, or even years) of expert engineering for a specific robot and its operating environment. Current control methodology is quite slow and labor intensive. I believe advances in machine learning have the potential to revolutionize robotics. In this talk, I will present new machine learning techniques we have developed that are tailored to robotics. I will describe in depth “Apprenticeship learning,” a new approach to high-performance robot control based on learning for control from ensembles of expert human demonstrations.

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