Biblio

Found 750 results
[ Author(Desc)] Title Type Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
R
Reinhart F, Steil JJ.  2014.  Efficient Policy Search with a Parameterized Skill Memory. :1400–1407.
Reinhart F, Steil JJ.  2009.  Reaching movement generation with a recurrent neural network based on learning inverse kinematics for the humanoid robot iCub. IEEE Conf. Humanoid Robotics. :323–330.
Reinhart F, Steil JJ.  2011.  State prediction: a constructive method to program recurrent neural networks. Artificial Neural Networks and Machine Learning – ICANN 2011 : 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I. 6791:159–166.
Reinhart F, Steil JJ, Huntsberger TL, Stoica A.  2010.  Tacking reduces bow-diving of high-speed unmanned sea surface vehicles. Int. Symp. Learning and Adaptive Behavior in Robotic Systems. :177–182.
Reinhart F, Neumann K, Aswolinskiy W, Steil JJ, Hammer B.  2018.  Maschinelles Lernen in technischen Systemen. Steigerung der Intelligenz mechatronischer Systeme. :pp.73-118.
Reinhart F, Lemme A, Steil JJ.  2012.  Representation and Generalization of Bi-manual Skills from Kinesthetic Teaching. IEEE-RAS International Conference on Humanoid Robots. :560–567.
Reinhart F, Steil JJ.  2016.  Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot. Procedia Technology. 26:12–19.
Reinhart F, Steil JJ.  2009.  Attractor-based computation with reservoirs for online learning of inverse kinematics. European Symposium Artificial Neural Networks (ESANN). :257–262.
Reinhart F, Steil JJ.  2011.  A constrained regularization approach for input-driven recurrent neural networks. Differential Equations and Dynamical Systems. 19:27–46.
Reinhart F, Steil JJ.  2012.  Regularization and stability in reservoir networks with output feedback. Neurocomputing. 90:96–105.
Reinhart F, Steil JJ.  2009.  Goal-directed movement generation with a transient-based recurrent neural network controller. Advanced Technologies for Enhanced Quality of Life. :112–117.
Reinhart F, Steil JJ.  2011.  Neural learning and dynamical selection of redundant solutions for inverse kinematic control. Proc. IEEE Int. Conf. Humanoid Robots. :564–569.
Reinhart F, Steil JJ.  2015.  Efficient Policy Search in Low-dimensional Embedding Spaces by Generalizing Motion Primitives with a Parameterized Skill Memory. Autonomous Robots. 38:331–348.
Reinhart F, Steil JJ.  2008.  Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot. Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award. 1:35–40.
Reinhart F, Steil JJ.  2012.  Learning Whole Upper Body Control with Dynamic Redundancy Resolution in Coupled Associative Radial Basis Function Networks. IROS. :1487–1492.
Rennekamp T..  2005.  Graphbasierte Bewegungsanalyse dynamischer Hindernisse zur Steuerung mobiler Roboter. Autonome Mobile Systeme 2005, 20. Fachgespräch Stuttgart. :295–301.
Rennekamp T., Homeier K., Kröger T..  2006.  Distributed Sensing and Prediction of Obstacle Motions for Mobile Robot Motion Planning. IEEE International Conference on Intelligent Robots and Systems. :4833–4838.
Rennekamp T..  2003.  Analyse und Kompensation von Fahrfehlern bei Mecanum-Wheel-Fahrzeugen. Autonome Mobile Systeme 2003, 19. Fachgespräch, Karlsruhe. :75–82.
Rhode M, Narioka K, Stein L, Steil JJ, Ernst M.  2019.  Goal-Related Feedback Guides Motor Exploration and Redundancy Resolution in Human Motor Skill Acquisition. PLOS Computational Biology. 15(3):1-27.
Richter M., Zech S., Westphal R., Klimesch Y., Gösling T..  2007.  Robotic cadaver testing of a new total ankle prosthesis model (german ankle system). Foot Ankle Int. 2007. :1276–1286.
Richter O, Reinhart F, Nease S, Steil JJ, Chicca E.  2015.  Device Mismatch in a Neuromorphic System Implements Random Features for Regression. Biomedical Circuits and Systems Conference (BioCAS), 2015 IEEE. :1–4.
Riechmann W., Strutz T., Stahs T..  1992.  Einsatzmöglichkeiten des Codierten Lichtansatzes in der Automobilindustrie. Querschnittseminar Bildverarbeitung der Deutschen Gesellschaft für Zerstörungsfreie Prüfung e.V Berlin.
Rieseler H..  1992.  Roboterkinematik - Grundlagen, Invertierung und Symbolische Berechnung.
Rieseler H., Haake S..  1989.  SKIP - A Symbolic Kinematics Inverversion Program.
Rieseler H., Wahl F..  1990.  Fast Symbolic Computation of the Inverse Kinematics of Robots. Proceedings IEEE International Conference on Robotics and Automation IEEE. :462–467.

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