Prof. Dr. Bernd Radig

Prof. Dr. Bernd Radig is principal researcher and member of the executive board of the Cluster of Excellence >Cognition for Technical Systems (CoTeSys)< (since 2006) and Full Professor for Image Understanding and Knowledge Based Systems, Fakultaet fuer Informatik, TU Muenchen (1986-2009). He was chairman and founder of the Association of Bavarian Research Cooperations (1993-2007), a unique network of scientists, specialising in challenging disciplines in accordance with the Bavarian companies. 1988 he founded together with colleagues from Univ. Erlangen and Univ. Passau and support from Bavarian enterprises the Bavarian Research Centre for Knowledge Based Systems (FORWISS), an institute common to the three universities. He holds the German Order of Merit (1992) and the award >Pro Meritis Scientiae et Litterarum< of the State of Bavaria (2002). His current activities are in real-time image sequence understanding for applications in robotics, sports or driver assistance systems.

• Model-Based Image Interpretation
• Objective Functions, Learning Objective Functions
• Face Detection, Face Model Fitting, Head Tracking
• Facial Expression Recognition, Emotion Recognition
• Person Localization, Person Tracking
• Driver Assistant Systems
• Multi-Robot Coordination

• Mudis : Since existing methods for human-machine interaction are often unintuitive, a lot of time is required for humans to adapt to the operation of a specific machine. In contrast, the MuDiS project aims at granting machines the ability to adapt to typical human behavior. The goal of the project is the development of a multimodal dialog system that considers various human communication channels such as facial expressions, spoken language and gestures for human-machine interaction. We perform experiments to determine the requirements for robots to interact with humans in an intuitive way. Insights gained from these human-human experiments are applied to the human-machine interface to grant robots the capability of participating in simple, every-day dialogs in various environments. To tackle this challenge, we unite researchers from diverse scientific areas, such as computer science, electrical engineering and psychology to reflect the interdisciplinary character of the project.

• CoP : The project aims at the unification of vision-based sensing in the CoP (Cognitive Perception) in the learning and planning system. On the one hand, CoP manages the interpretation of different kinds of sensors and on the other hand it automatically acquires and maintains the knowledge about the world and objects in the world. CoP selects sensors and sensor interpretation algorithms based on their expected utility. To this end, CoP learns and improves intersensor and inter-algorithmic models for method seleciton from experience. Especially the vision system, the major sensor we use, provides several automatic model improving techniques. Improved models accelerate the perception process and provide more robust results.

• Face Image Analysis : As robots emerge from their classical domain - factories - to be included in every day life, they need to gain new abilities besides those needed in manufacturing. They need not only to support humans, but also be able to socialize with their users to enhance the interactant experience and allow for social bonding. Recent progress in the field of Computer Vision allows intuitive interaction via gesture or facial expressions between humans and technical systems. Recent research aims at enabling machines to utilize communication channels natural to human beings, such as gesture or facial expressions. Humans interpret emotion from video and audio information and heavily rely on this information during every-day communication. Therefore, knowledge about human behavior, intention, and emotion is necessary to construct convenient human-machine interaction mechanisms. The human face provides much of the information that is passed between humans in every-day communication. Although most of this information is passed on a subconscious level, we still rely on the interaction partner's facial expression to determine emotional state or attention to form a prediction of his or her reaction.

• MuJoVision : The Multi-Joint Vision project aims at providing image data obtained simultaneously from a large number of cameras in real time for further processing in Computer Vision applications. It involves the integration of a flexibly expandable architecture and interface for distributed Computer Vision applications. The project is part of a Multi-Joint Action scenario developed for the CoTeSys cluster of excellence, and the data obtained is used to generate tracking information for Human-Computer Interaction and monitoring tasks.

• Cogito : A key challenge for the next generation of autonomous robots is the reliable and efficient accomplishment of prolonged, complex, and dynamically changing tasks in the real world. One of the most promising approaches to realizing these capabilities is the plan-based approach to robot control. In the plan-based approach, robots produce control actions by generating, maintaining, and executing plans that are tailored for the robots' respective tasks. Plans are robot control programs that a robot can not only execute but also reason about and manipulate. Thus a plan-based controller is able to manage and adapt the robot's intended course of action — the plan — while executing it and can thereby better achieve complex and changing goals. The use of plans enables these robots to flexibly interleave complex and interacting tasks, exploit opportunities, quickly plan their courses of action, and, if necessary, revise their intended activities. One of the grand visions in the area of plan-based robot control is the realization of general autonomous robot control programs that can adapt themselves to the environments they are to operate in and to the distribution of complex tasks they are to perform. An instance of this grand vision is a pre-programmed household robot that knows how to clean a kitchen, how to operate a dishwasher, and so on. Being installed in a new environment it specializes its general plans to the specifics of the household and learns to manage the specific agenda of household chorus that is given to it. The robot also has to learn about the pitfalls of its tasks and its environment and avoid them through foresight. Our research field is still far away from realizing such competent robotic agents.

• ProbCog : ProbCog designs and implements a statistical relational learning system that supports efficient learning and inference in relational domains, focusing mainly on probabilistic logical models. Based on a common data model, we integrate various representation formalisms and have successfully extended the expressiveness and practical applicability of existing approaches. ProbCog thus provides a coherent probabilistic framework that enables cognitive technical systems to deal with a high degree of uncertainty and complexity in a variety of application contexts, including adaptive plan generation for under-specified tasks and heuristic generation.

• Agilo : Robotic soccer has become a standard 'real-world' testbed for autonomous multi robot control. In robot soccer (mid-size league) two teams of four autonomous robots — one goal keeper and three field players — play soccer against each other. The soccer field is four by nine meters big. The key characteristics of mid-size robot soccer are that the robots are completely autonomous. Consequently, all sensing and all action selection is done onboard of the individual robots. Skillful play requires our robots to recognize objects, such as other robots, field lines, and goals, and even entire game situations. In the AGILO project we investigate how probabilistic visuomotoric autonomous robot controllers that are capable of learning can meet these challenges. The AGILO robot controllers employ game state estimation and situated action selection based on automatically learned control mechanisms.

• Bavarian Research Center for Knowledge-based Systems (FORWISS)
• Arbeitsgemeinschaft der bayerischen Forschungsverbuende (http://www.abayfor.de/abayfor)