Adaptive Artificial Limbs A Real-Time Approach to Prediction and Anticipation By Patrick M. Pilarski, Michael Rory Dawson, Thomas Degris, Jason P. Carey, K. Ming Chan, Jacqueline S. Hebert, and Richard S. Sutton P redicting the future has long been regarded as a powerful means to improvement and success. The ability to make accurate and timely predictions enhances our ability to control our situation and our environment. Assistive robotics is one prominent area in which foresight of this kind can bring improved quality of life. In this article, we present a new approach to acquiring and maintaining predictive knowledge during the online ongoing operation of an assistive robot. The ability to learn accurate, temporally abstracted predictions is shown through two case studies: 1) able-bodied myoelectric control of a robot arm and 2) an amputee's interactions with a myoelectric training robot. To our knowledge, this research is the first demonstration of a practical method for real-time prediction learning during myoelectric control. Our approach therefore represents a fundamental tool for addressing one major Digital Object Identifier 10.1109/MRA.2012.2229948 Date of publication: 8 March 2013 1070-9932/13/$31.00©2013IEEE MARCH 2013 * IEEE ROBOTICS & AUTOMATION MAGAZINE * 53