Reinforcement learning applications have evolved rapidly throughout the last decade. Recently the DFKI successfully implemented a reinforcement learning (RL) algorithm that uses error potentials evoked by error recognition in the human brain for human-robot interaction. The algorithm makes it possible to map gesture inputs to robotic actions using error potentials as feedback (reward in RL).
New situations in real world applications could make it necessary to adapt the approach. The master thesis will focus on investigating approaches that make it possible to add gestures and actions, while retaining already learned knowledge. For evaluation of the developed algorithm data has been recorded. The first results will be presented.
