The goal of the Machine Perception and Interaction (MPI) lab is to develop methods and techniques to extract meaningful information from sensor data where such data emerges from complex physical systems such as robots and/or sensor networks. The extraction of this information is to facilitate good interaction between humans and systems, as well as between systems. Our investigations aim to provide semantically rich representations of data using AI techniques within machine learning and automated reasoning.  We further study the impact of our methods by studying the quality of interaction it provides with humans and other agents. Our research is situated in a number of different topics and fields where we work together with industry and other academic partners, and include social robotics, smart home environments, multi-agent systems and more.