• The "Research Institute for Cognition and Robotics - CoR-Lab"

• What does CoR-Lab offer ?
  • Technology transfer
  • Talks and presentations on cognitive and learning technology
  • Training of highly-qualified personnel
  • Cooperative FuE projects in research and teaching
  • Information exchange
  • Cooperation in individual measures
  • Contact for students projects
  In order to face the enormous challenges that these question imply for robotics, the „Research Institute for Cognition and Robotics (CoR-Lab)“ was founded at the Bielefeld University in July 2007 under main participation of the research groups Neuroinformatics (Prof. Dr. H. Ritter, Prof. Dr. J. J. Steil) and Applied Computer Science (Prof. Dr. G. Sagerer), with support by the ministry of Innovation, Science, Research and Technology of North Rhine-Westphalia and in a strategic partnership with the industrial partner Honda Research Institute Europe GmbH (HRI-EU). In this ongoing partnership, HRI-EU contributed two ASIMO robots for research projects in a joint graduate school from 2007-2012. The special relationship between Honda and CoR-Lab has been renewed in 2012 for further four years and continues with new research projects in robotics, machine learning and driver assistance.
  The participating research groups in CoR-Lab are widely known for their work in artificial cognition, computer vision, neural networks and machine learning, and human-robot interaction. They are in command of a unique wealth of robot platforms including two humanoid iCub robots, several Nao robots, anthropomorphic robot hand, Kuka LWR compliant arms, a bionic handling assistant, and further service robots.
  The acquisition of competencies in these wide fields of intelligent systems technology since its inauguration in 2007, and its growing focus on applied research enables CoR-Lab to serve more and more as a center for the transfer of advanced intelligence technology from fundamental research towards industrial application in the region Ostwestfalen-Lippe. Through joint projects with regional industry partners it supports the domestic medium-sized economy by organizing transfer of excellent research, e.g. in a transfer chain reaching from the Bielefeld Center of Excellence in Cognitive Interaction Technology CITEC through CoR-Lab to application partners. With this aim, CoR-Lab also takes the lead for Bielefeld University’s contribution to the BMBF Spitzencluster it’s-owl (Intelligent Technical Systems).

• Projects

• it's OWL

• Add Intelligence to Machines.
  High-tech for tomorrow’s markets. A technology network encompassing economy and science about to set world standards for intelligent products and production systems is growing in OstWestfalenLippe. The clusterIntelligent Technical Systems OstWestfalenLippe– in short, it’s OWL – is regarded as a pioneer for Industry 4.0 and makes important contributions to Germany’s competitiveness as an industry location. Having been awarded in the Leading- Edge Cluster Competition of the Federal Ministry of Education and Research, is a seal of quality for it’s OWL.
  174 companies, research institutes and organizationscooperate within the it’s OWL network. In a joint effort of economy and science they approach the innovation leap from mechatronics towards Intelligent Technical Systems. World market leaders in the fields of mechanical engineering, electrical and automotive supply industries collaborate with top-level research institutes.
  45 projectswith a total budget of 100 million Euro will develop technologies for new generation products and production systems – ranging from automation and drive solutions to machines, vehicles, household appliances to Smart Grids and networking production plants.
  The Federal Ministry of Education and Research sustains these activities with 40 million Euro.
  The technologyplatform will be made usable for numerous producing companies. This is a strong impulse for growth and employment in the OstWestfalenLippe region.
  
  Technology Concept
  Resulting from a joint effort of engineering sciences and informatics Intelligent Technical Systems
  
  • interact with the environment and adapt autonomously (adaptive),
  • cope with unexpected situations in a dynamic surrounding (robust),
  • anticipate the effects of diverse influences on the basis of experiential knowledge (anticipatory),
  • 和consider individual user behavior (user-friendly).
  Intelligent Technical Systems
  • help creating new functionalities of products and production systems and provide easier handling for users,
  • improve development, installation, maintenance, and life cycle management,
  • improve reliability, safety, and availability,
  • provide for a more efficient use of resources like energy and materials,
  • 和allow for individualized and changeable production processes.
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• FlexIRob——运动学习在你的指尖

• Enabling robots to become co-workers that collaborate with humans efficiently and in safely is a major goal of current robotics research. At Bielefeld University's CoR-Lab, researchers use learning and interaction technology for control of compliant robots such as the KUKA Lightweight Robot (LWR) to realize such human-robot interaction. Research results are continuously integrated in the showcase robotic system “FlexIRob” providing a testbed for flexible robotic co-worker and advanced human-robot collaboration scenarios.
  
  In its current setup, FlexIRob allows to teach a redundant robot various Nullspace constraints in different areas of the workspace. Users with no particular robotics knowledge can perform this task in physical interaction with the compliant robot, for example to reconfigure of a working cell environment. After a short training phase, the learned adaptive mapping solves the inverse kinematics problem of the robot. It is embedded in the motion controller of the system, hence allowing for execution of arbitrary motions in task space, respecting the learned Nullspace constraints. This is a large step towards our vision of a flexible robotic coworker system, because it avoids the complex manual programming that standard methods for this task have previously required.
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• AMARSi - Adaptive Modular Architectures for Rich Motor Skills

• AMARSi is a EU-funded research project in the Seventh Framework Programme. The project is a large scale integration project hosted in the category Information and Communication Technologies , unit E5: Cognitive Systems, Interaction and Robotics .
  
  Motor skills of humans and animals are still utterly astonishing when compared to robots. AMARSi aims at a qualitative jump in robotic motor skills towards biological richness. AMARSi' objective: the experimentation and demonstration of rich motor skills on the iCub humanoid robot and on the quadruped Oncilla. Rich motor skills in robots will have a tremendous impact on our society. Dexterous and skillful motion in robots will make them more suitable for a large number of tasks. The compliant and natural movements will make them blend into everyday routines, safe and psychologically acceptable.
  
  Research in AMARSi focuses on three main areas: biology, robotics and software methods. Seven research subjects (or Work Packages) are tightly integrated.
  
  • Human Motor Primitives - Work Package 1
  • Compliant Systems - Work Package 2
  • Morphological Computation - Work Package 3
  • Adaptive Modules - Work Package 4
  • Learning - Work Package 5
  • Architectures - Work package 6
  • Robotic Experimentation - Work package 7
  • Dissemination and Training - Work package 8
  • Management and Coordination - Work package 9
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• Research Groups

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  • Cognitive Robotics and Learning

  • 研究小组组织的边界achine 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.
    
    Main methodological foci are neural learning methods, in particular recurrent reservoir networks, and the transfer of other machine learning approaches to interactive scenarios, which require high computational efficiency and online-learning capabilities. Of particular interest in this domain are generative approaches to allow for behavior generation along with classification or prediction. Important methodological aspects of generative models are covered by the projects "ALEGRO" and "Theory of generative reservoirs".
    The projects "Neural Learning of Flexible Full Body Motion" and "Goal-directed Imitation Learning from Humans" pursue the autonomous bootstrapping of motor control and skill acquisition by imitation learning. Central goal is to understand cognitive and developmental aspects related to motor learning and learning by imitation. The research group follows a synthetic approach to tackle important questions in this context by implementing computational models of these processes.
    
    The research group also contributes neural learning methods and robotic experimentation to the FP7-IP large scale project AMARSi -- Adaptive Modular Architectures for Rich Motor Skills, which is coordinated by Prof. Steil.
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  • Cognitive Systems Engineering

  • Cognitive robotics is an experimental research activity that combines research efforts in mechatronics, informatics and the cognitive sciences. Creating cognitive robots requires building systems that can adapt their behavior to environments that are complex, rapidly changing, and that cannot be completely modeled in preface. Nowadays, many of the resulting challenges can be successfully addressed on the level of individual algorithms or by advanced robotics hardware tuned for specific scenarios.
    In order to provide an avenue for robotic systems to become useful in every-day human settings and as a prerequisite for entering the consumer market, these attributes must be ensured on a system level that encompasses the web of skills advanced robots must be capable of. The aim of the cognitive systems engineering group is to investigate software architectures and engineering principles that allow to efficiently integrate, implement and bring together this web of different skills in order to build complex cognitive robotic systems.
    
    A fundamental prerequisite for mastering the (technical) integration of complex robotics systems is the availability and in-depth understanding of robotics middleware technology. For this reason and despite the availability of frameworks such as ROS, we decided to further develop a custom, but very light-weight and open robotics middleware termed RSB. This so called Robotics Service Bus (RSB) is an efficient message-oriented, event-driven middleware aiming at scalable integration of robotics systems in heterogeneous environments. It is fully open source and available for various operating system, featuring implementations for a number of popular programming languages. Due to our involvement in the HUMAVIPS EU project, specific platform support will be made available for the NAO humanoid robot at the HUMAVIPS Open Portal page. Please find more information and a comparison to ROS concepts at the corresponding project website.
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  • Hybrid Society

  • When taking the reasoning of current robotic research seriously we have to face the possibility that in the future humans and robots will live together in a shared environment and form a hybrid society of natural and artificial agents. We can thus expect to see more and more humans interacting with robots. This vision opens new research questions and opportunities that we want to tackle in the research group „Hybrid Society“.
    
    On the one hand, robots will be able to profit from interactions with humans. As humans are experts when it comes to understanding and handling tasks in the real world, they are a valuable source of information for robots. But how can robots make use of this information? In our group we target at enabling robots to be tutored by humans. Research has shown that adults provide specifically designed input to infants in tutoring situations. We expect to see similar behavior in human-robot tutoring situations provided that the robot displays a certain degree of social behavior. Based on such behavior we are currently determining ways for the robot to understand the meaning of a demonstrated action and to detect when an action is being demonstrated in order to be learned by the robot.
    
    另一方面,机器人的主要目标help humans and support them in their daily activities. However, in order for robots to be really helpful they not only must be able to learn and carry out tasks and actions. They must also display socially acceptable behavior and be understandable and predictable for their interaction partners. This entails not only to the propositional content of an interaction as modelled by a dialog management component, which enables to build a common ground of shared information between the user and the robot. Also, the robot should be able to take the affective components of an interaction into account by reading the human’s emotional expressions and by producing appropriate emotional signals. We suppose that such an alignment of emotional behavior will make interaction with the robot not only more satisfying but also more efficient.

• Technology Transfer

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  • • Technology Transfer

    • Transfer and cooperative offers for enterprises
      
      The next generation of intelligent robots will be used as assistants in the industrial range and will bring along own knowledge and abilities, which will allow them to act autonomously. The vision is a custom-made, industrial solution for enterprises with a maximum of variability, simple handling and flexibility.
      这个愿景给人机c带来新的挑战ooperation and communication like security in interaction, and needs increased robustness, efficiency, accuracy and economy.
      
      CoR-Lab aims to enhance the respective potential of learning and cognitive technology in the region East-Westphalia and Lippe (OWL) by cooperation with regional industry and offers to help enterprises increasing their competitive ability in this upcoming field of high-technology through joint workshops, FuE projects, and networking. The training of highly-qualified engineers in CoR-Lab's Graduate School also contributes to the innovative strength and the economic development of the region.