Creativity in Robot Manipulation with Deep Reinforcement Learning

Abstract

Deep Reinforcement Learning (DRL) has emerged as a powerful control technique in robotic science. In contrast to control theory, DRL is more robust in the thorough exploration of the environment. This capability of DRL generates more human-like behaviour and intelligence when applied to the robots. To explore this capability, we designed challenging manipulation tasks to observe robots strategy to handle complex scenarios. We observed that robots not only perform tasks successfully, but also transpire a creative and non intuitive solution. We also observed robot's persistence in tasks that are close to success and its striking ability in discerning to continue or give up.