One fundamental issue of autonomous robots in task domains is its capability to learn new skills and to re-use past experiences under different situations as efficient, intuitive and reliable as possible. A promising mechanism to achieve that is via learning from demonstrations or observations. In this talk, we present a novel learning method that generates compact and general semantic models to infer human activities. We propose a method that allows robots to obtain and determine a higher-level understanding of a demonstrator’s behavior via semantic representations. First, the low-level information is extracted from the sensory data, then a meaningful semantic description, the high-level, is obtained by reasoning about the intended human behaviors. The introduced method has been assessed on different robots, e.g. the iCub, REEM-C, and TOMM, with different kinematic chains and dynamics. Furthermore, the robots use different perceptual modalities, under different constraints and in several scenarios ranging from making a sandwich to driving a car assessed on different domains (home-service and industrial scenarios). Each of the studied scenarios poses distinct and challenging levels of complexity to demonstrate, that our method does not depend on the analyzed task, thus presenting a major benefit compared to classical reasoner approaches. Another important aspect of our approach is its scalability and adaptability toward new activities, which can be learned on-demand. Overall, the presented compact and flexible solutions are suitable to tackle complex and challenging problems for autonomous robots.
Dr. Karinne Ramirez-Amaro is a Post-doctoral researcher at the Chair for Cognitive Systems (ICS). She completed her Ph.D. (summa cum laude) at the Department of Electrical and Computer Engineering at the Technical University of Munich (TUM). She performed her Ph.D. under the supervision of Prof. Gordon Cheng and she joined ICS in January 2013. From October 2009 until Dec 2012, she was a member of the Intelligent Autonomous Systems (IAS) group headed by Prof. Michael Beetz. She received a Master degree in Computer Science (with honors) at the Center for Computing Research of the National Polytechnic Institute (CIC-IPN) in Mexico City, Mexico in 2007. Dr. Ramirez-Amaro received the Laura Bassi award granted by TUM and the Bavarian government to conduct a one-year research project in December 2015. For her doctoral thesis, she was awarded the price of excellent Doctoral degree for female engineering students, granted by the state of Bavaria, Germany in September 2015. In addition, she was granted a scholarship for a Ph. D. research by DAAD – CONACYT and she received the Google Anita Borg scholarship in 2011. Currently, she is involved in the EU FP7 project Factory-in-a-day and in the DFG-SFB project EASE. Her research interests include Artificial Intelligence methods, Semantic Representations, Assistive Robotics, Expert Systems, and Human Activity Recognition and Understanding.