Bootstrapping humanoid robot skills by extracting semantic representations of human-like activities from virtual reality
Karinne Ramírez-Amaro, Tetsunari Inamura, Emmanuel Dean‐Leon, Michael Beetz, Gordon Cheng · 2014
Advancements in Virtual Reality have enabled well-defined and consistent virtual environments that can capture complex scenarios, such as human everyday activities. Additionally, virtual simulators (such as SIGVerse) are designed to be user-friendly mechanisms between virtual robots/agents and real users allowing a better interaction. We envision such rich scenarios can be used to train robots to learn new behaviors specially in human everyday activities where a diverse variability can be found. In this paper, we present a multi-level framework that is capable to use different input sources such as cameras and virtual environments to understand and execute the demonstrated activities. Our presented framework first obtains the semantic models of human activities from cameras, which are later tested using the SIGVerse virtual simulator to show new complex activities (such as, cleaning the table) using a virtual robot. Our introduced framework is integrated on a real robot, i.e. an iCub, which is capable to process the signals from the virtual environment to then understand the activities performed by the observed robot. This was realized through the use of previous knowledge and experiences that the robot has learned from observing humans activities. Our results show that our framework was able to extract the meaning of the observed motions with 80% accuracy of recognition by obtaining the objects relationships given the current context via semantic representations to extract high-level understanding of those complex activities even when they represent different behaviors.