Transfer Learning of Complex Motor Skills on the Humanoid Robot Affetto

Although autonomous robots can perform particularly well at highly specific tasks, learning each task in isolation is a very costly process, not only in terms of time but also in terms of hardware wearout and energy usage. Hence, robotic systems need to be able to adapt quickly to new situations in order to be useful in everyday tasks. One way to address this issue is transfer learning, which aims at reusing knowledge obtained in one situation,...