The effortless ease with which humans move our arms, our eyes, even our lips when we speak masks the true complexity of the control processes involved. This is evident when we try to build machines to perform human control tasks. While computers can now beat grandmasters at chess, no computer can yet control a robot to manipulate a chess piece with the dexterity of a six-year-old child. I will review our recent work on how the humans learn to make skilled movements covering structural learning and generalization, how we learn the dynamics of tools and how we make decisions in the face of uncertainty.
Daniel M Wolpert (University of Cambridge)
Daniel Wolpert read medical sciences at Cambridge and clinical medicine at Oxford. After working as a medical doctor for a year he completed a PhD in the Physiology Department at Oxford. He then worked as a postdoctoral fellow at MIT, before moving to the Institute of Neurology, UCL. In 2005 he took up the post of Professor of Engineering for the Life Sciences at the University of Cambridge and is a Fellow of Trinity College. His research interests are computational and experimental approaches to human sensorimotor control (www.wolpertlab.com).
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