Kognitive Robotik und Lernen

The research group is organized at the border of machine learning, cognition and human-robot interaction. The ultimate goal is to enable interactive learning in human-machine cooperation. The research group investigates efficient and life-long learning methods for behavior generation, motor learning and visual object recognition to maximize the autonomy and adaptability of robotic systems. In particular, imitation learning and bootstrapping processes are central research areas. Application areas of this research group comprise behavior learning and generation for ASIMO, iCub, the light-weight KUKA LWR robot arm and other robots, visual online learning for object recognition, and autonomous learning approaches to generate complex time series including audio and video signals.