As our contribution to the effort of developing methods to make robots more adaptive and robust to dynamic environments, we have proposed our method of ‘minimal surprise’ in a series of previous works. In a multi-robot setting, we use evolutionary computation to evolve pairs of artificial neural networks: an actor network to select motor speeds and a predictor network to predict future sensor input. By rewarding for prediction accuracy, we give robots an innate, task-independent motivation to behave in structured and thus, predictable ways. While we previously focused on feasibility studies using abstract simulations, we now present our first results using realistic robot simulations and first experiments with real robot hardware. In a centralized online and onboard evolution approach, we show that minimize surprise works effectively on Thymio II robots in an area cleaning scenario.
|Title of host publication||Springer Proceedings in Advanced Robotics|
|Number of pages||12|
|Publication status||Published - 2021|