Online robot learning by reward and punishment for a mobile robot

The existing robot learning methods require specifically defined goals. We aim to produce a more flexible behavior. We present our work which a human observer can influence the robot behavior. The robot learns by reward and punishment from a human in real-time. To examine the developed approach, we perform a control system for a color-following task as an example. A physical robot is used to perform the experiments. Experimental results show the emergence of learned behaviors. We discussed the factors that influence the learning process.