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Cognition-Enabled Robot Control for the Realization of Home Chore Task Intelligence Cognition-Enabled Robot Control for the Realization of Home Chore Task Intelligence Michael Beetz Intelligent Autonomous Systems Technische Universität München LCCC & Rosetta Symposium on Robot Skill Learning and Cognition April 18, 2012 Introduction Cognition-enabled Control Housework Principles Everyday Robot M

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Abstraction by Structure Introduction Structural Representations Structural Models Conclusion References Abstraction by Structure Carl Henrik Ek, Danica Kragic {chek, danik}@csc.kth.se Royal Institute of Technology April 18, 2012 Ek, Kragic KTH Abstraction by Structure Introduction Structural Representations Structural Models Conclusion ReferencesComputer Vision and Robotics at KTH i) Head of grou

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ON HUMAN ACTION Volker Krüger Dept. of Mechanical and Production Engineering Aalborg University vok@m-tech.aau.dk Saturday, April 21, 2012 () mailto:vok@m-tech.aau.dk mailto:vok@m-tech.aau.dk Observation World Model Reproduction/Recognition Cleaning the Kitchen Saturday, April 21, 2012 () Observation World Model Reproduction/Recognition Cleaning the Kitchen What is the action? Saturday, April 21,

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Microsoft PowerPoint - 20120418_LCCC.ppt [Compatibility Mode] Transferring Human Skills to Humanoid Robots Dongheui Lee dhlee@tum.de bDynamic Human-Robot-Interaction for Automation Systems (HRI) Lab Department of Electrical Engineering and Information Technology Technical University of MunichTechnical University of Munich Transferring Human Skills to Humanoid Robots Movements • learning Manipulati

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untitled Automation & Robotics Research Institute (ARRI) The University of Texas at Arlington, USA F.L. Lewis, Dan Popa Talk available online at http://ARRI.uta.edu/acs Reinforcement Methods for Autonomous Online Learning of Optimal Robot Behaviors Supported by : NSF - Paul Werbos ARO- Sam Stanton AFOSR- Fariba Fahroo Bristol Robotics Lab, University of Bristol, UK Guido Herrmann  Optimal Control

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Common Knowledge of Industrial Robots - Knowledge and Skill Representation for Industrial Robotics Common Knowledge of Industrial Robots Common Knowledge of Industrial Robots Knowledge and Skill Representation for Industrial Robotics Jacek Malec Robotics and Semantic Systems Department of Computer Science Lund University April 18, 2012 LCCC Workshop, 18.04.2012 1(32) Common Knowledge of Industrial

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bruyninckx.pptx 1 Hanyang University Il Hong Suh 2012.4.17 Evolution of Species Bacteria cyanobacteria trilobite fish Amphibia Dinosaur Mammalia Human 3.8 billon y ears 1 billon ye ars 700 million y ears eukaryote 1.8 million years Origin of L anguage Darwinian Creature Skinnerian Creature Popporian Creature Gregorian Creature 542 million y ears Cambrian ex plosion 2 billon ye ars Simple cel ls Co

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Slide 1 Challenges in adapting imitation and reinforcement learning to compliant robots Sylvain Calinon Learning & Interaction Group Department of Advanced Robotics Italian Institute of Technology (IIT) www.programming-by-demonstration.org/learning-and-interaction/ LCCC Symposium on Robotic Skill Learning and Cognition Lund, 17-19 April 2012 Italian Institute of Technology (IIT) • Created in 2003,

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movement intelligence before control a story on where to look in biology Patrick van der Smagt bionics und assistive robotics Institute of Robotics and Mechatronics DLR Oberpfaffenhofen biomimetic robotics and machine learning Faculty for Informatics Technische Universität München biology-inspired approach to robotics #1: movement intelligence is caused by superior control (“a robot with a biologi

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