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In this work we explore the use of reinforcement learning (RL) to help withhuman decision making, combining state-of-the-art RL algorithms with anapplication to prosthetics. Managing human-machine interaction is a problem ofconsiderable scope, and the simplification of human-robot interfaces isespecially important in the domains of biomedical technology and rehabilitationmedicine. For example, amputees who control artificial limbs are often requiredto quickly switch between a number of control actions or modes of operation inorder to operate their devices. We suggest that by learning to anticipate(predict) a user's behaviour, artificial limbs could take on an active role ina human's control decisions so as to reduce the burden on their users.Recently, we showed that RL in the form of general value functions (GVFs) couldbe used to accurately detect a user's control intent prior to their explicitcontrol choices. In the present work, we explore the use of temporal-differencelearning and GVFs to predict when users will switch their control influencebetween the different motor functions of a robot arm. Experiments wereperformed using a multi-function robot arm that was controlled by musclesignals from a user's body (similar to conventional artificial limb control).Our approach was able to acquire and maintain forecasts about a user'sswitching decisions in real time. It also provides an intuitive and reward-freeway for users to correct or reinforce the decisions made by the machinelearning system. We expect that when a system is certain enough about itspredictions, it can begin to take over switching decisions from the user tostreamline control and potentially decrease the time and effort needed tocomplete tasks. This preliminary study therefore suggests a way to naturallyintegrate human- and machine-based decision making systems.

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb