Biblio

Found 255 results
Author [ Title(Asc)] 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
Rolf M, Gienger M, Steil JJ.  2013.  Robot control with bootstrapping inverse kinematics.
Reinhart F, Steil JJ.  2011.  Reservoir regularization stabilizes learning of Echo State Networks with output feedback. Proc. European Symposium on Artificial Neural Networks. :59–64.
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.
Neumann K, Rolf M, Steil JJ.  2013.  Reliable Integration of Continuous Constraints into Extreme Learning Machines. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems. 21:35–50.
Neumann K.  2014.  Reliability of Extreme Learning Machines. :155.
Neumann K, Emmerich C, Steil JJ.  2012.  Regularization by Intrinsic Plasticity and its Synergies with Recurrence for Random Projection Methods. Journal of Intelligent Learning Systems and Applications. 4:230–246.
Reinhart F, Steil JJ.  2012.  Regularization and stability in reservoir networks with output feedback. Neurocomputing. 90:96–105.
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.
Steil JJ, Ritter H.  1999.  Recurrent Learning of Input-Output Stable Behaviour in Function Space: A Case Study with the Roessler Attractor. Proc. Int. Conf. Artificial Neural Networks. :761–766.
Emmerich C, Reinhart F, Steil JJ.  2010.  Recurrence Enhances the Spatial Encoding of Static Inputs in Reservoir Networks. Proc. Int. Conf. Artificial Neural Networks. :148–153.
Muxfeldt A, Steil JJ.  2018.  Recovering from Assembly Errors by Exploiting Human Demonstrations. 51st CIRP Conference on Manufacturing Systems.
Hammer B, Schrauwen B., Steil JJ.  2009.  Recent advances in efficient learning of recurrent networks. European Symposium on Artificial Neural Networks. :213–226.
Mohammadi P, Malekzadeh M, Kodl J, Mukovskiy A, Wigand D, Giese M, Steil JJ.  2018.  Real-time Control of Whole-body Robot Motion and Trajectory Generation for Psychotherapeutic Juggling in VR. IROS 2018.
Lebedev DV, Steil JJ, Ritter H.  2003.  Real Time Path Planning in Dynamic Environment: a Comparison of Three Neural Network Models. Proc. IEEE Int. Conf. Systems, Man, and Cybernetics. :3408–3413.
Mohammadi P, Hoffmann EMingo, Muratore L, Tsgarakis N, Steil JJ.  2019.  Reactive Walking Based on Upper-Body Manipulability: An application to Intention Detection and Reaction. ICRA.
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.
Soltoggio A, Lemme A, Reinhart F, Steil JJ.  2013.  Rare neural correlations implement robotic conditioning with reward delays and disturbances. Frontiers in Neurorobotics. 7:6.
P
Lin H-C, Smith J, Babarahmati KKouhkiloui, Dehio N, Mistry M.  2018.  A Projected Inverse Dynamics Approach for Multi-arm Cartesian Impedance Control. Int. Conf. Robotics and Automation.
Wojtynek M, Steil JJ, Wrede S.  2019.  Plug, Plan and Produce as Enabler for easy Workcell Setup and Collaborative Robot Programming in Smart Factories. KI - Künstliche Intelligenz. 33(Special Issue: Smart Production 2):151–161.
Röthling F, Haschke R, Steil JJ, Ritter H.  2007.  Platform Portable Anthropomorphic Grasping with the Bielefeld 20-DOF Shadow and 9-DOF TUM Hand. Proc. Int. Conf. on Intelligent Robots and Systems (IROS). :2951–2956.
Hammer B, Steil JJ.  2002.  Perspectives on Learning with Recurrent Neural Networks. Proc. European Symposium Artificial Neural Networks. :357–368.
Shareef Z.  2015.  Path planning and trajectory optimization of delta parallel robot. Department of Mechanical Engineering. PhD Thesis
Aswolinskiy W, Steil JJ.  2016.  Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks. Proceedings of the Workshop on New Challenges in Neural Computation.

Pages