Interactive reinforcement learning through speech guidance in a domestic scenario

Recently robots are being used more frequently as assistants in domestic scenarios. In this context we train an apprentice robot to perform a cleaning task using interactive reinforcement learning since it has been shown to be an efficient learning approach benefiting from human expertise for performing domestic tasks. The robotic agent obtains interactive feedback via a speech recognition system which is tested to work with five different microphones concerning their polar patterns and distance to the teacher to recognize sentences in different instruction classes. Moreover, the reinforcement learning approach uses situated affordances to allow the robot to complete the cleaning task in every episode anticipating when chosen actions are possible to be performed. Situated affordances and interaction allow to improve the convergence speed of reinforcement learning, and the results also show that the system is robust against wrong instructions that result from errors of the speech recognition system.