The next generation of robots will soon get out of the secure and predictable environment of factories and will face the complexity and unpredictability of our daily environments. To avoid that robots fail lamely at the task they are programmed to do, robots will need to adapt on the go.
Yoshua Bengio is presenting techniques from machine learning to allow robots to learn strategies to enable them to react rapidly and efficiently to changes in the environment. Learning the set of feasible solutions will be preferred over learning optimal controllers.
He reviews methods his team has developed to allow instantaneous reactions to perturbation, leveraging on the multiplicity of feasible solutions. He is presenting applications of these methods for compliant control during human-robot collaborative tasks and for performing fast motion in sport, such as when playing golf with moving targets.
The talk will conclude with examples in which robots achieve super-human capabilities for catching fast moving objects with a dexterity that exceeds that displayed by human beings.