Found 231 results
Author [ Title(Desc)] 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 
Malekzadeh MS, Calinon S, Bruno D, Caldwell DG.  2014.  Learning by Imitation with the STIFF-FLOP Surgical Robot: A Biomimetic Approach Inspired by Octopus Movements.
Weng S., Steil JJ.  2003.  Learning Compatibitlity Functions for Feature Binding and Perceptual Grouping. Proc. of Int. Conference Artificial Neural Networks. LNCS 2714:60–67.
Rolf M, Steil JJ, Gienger M.  2010.  Learning Flexible Full Body Kinematics for Humanoid Tool Use. Int. Symp. Learning and Adaptive Behavior in Robotic Systems (Best Paper Award). :171–176.
Kubus D, Rayyes R, Steil JJ.  In Press.  Learning Forward and Inverse Kinematics Maps Efficiently. IROS 2018.
Malekzadeh M, Queißer J, Steil JJ.  2015.  Learning from demonstration for Bionic Handling Assistant robot.
Alizadeh T, Malekzadeh MS, Barzegari S..  2016.  Learning from demonstration with partially observable task parameters using dynamic movement primitives and Gaussian process regression. 2016 IEEE International Conference on Advanced Intelligent Mechatronics (AIM).
Neumann K, Rolf M, Steil JJ, Gienger M.  2010.  Learning Inverse Kinematics for Pose-Constraint Bi-Manual Movements. From Animals to Animats 11. 11th International Conference on Simulation of Adaptive Behavior, SAB 2010. Proceedings. 6226
Rayyes R, Kubus D, Hartmann C, Steil JJ.  2017.  Learning Inverse Statics Models Efficiently. arXiv.
Weirich A, Haumann C, Steil JJ, Schüler S..  2011.  Learning Lab - Physical Interaction with Humanoid Robots for Pupils. Proc. Robotics in Education. :21–28.
Weng S, Wersing H, Steil JJ, Ritter H.  2006.  Learning Lateral Interactions for Feature Binding and Sensory Segmentation from Prototypic Basis Interactions. IEEE Trans. Neural Networks. 17:843–862.
Kober J, Gienger M, Steil JJ.  2015.  Learning Movement Primitives for Force Interaction Tasks. ICRA. :3192–3199.
Neumann K, Steil JJ.  2015.  Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations. Robotics and Autonomous Systems. 70:1–15.
Malekzadeh M, Queißer J, Steil JJ.  2016.  Learning the end-effector pose from demonstration for the Bionic Handling Assistant robot.
Soltoggio A, Reinhart F, Lemme A, Steil JJ.  2013.  Learning the rules of a game: neural conditioning in human-robot interaction with delayed rewards.
Bruno D, Calinon S, Malekzadeh MS, Caldwell DG.  2015.  Learning the stiffness of a continuous soft manipulator from multiple demonstrations. International Conference on Intelligent Robotics and Applications.
Freire A, Lemme A, Steil JJ, Baretto G.  2012.  Learning visuo-motor coordination for pointing without depth calculation. Proc. European Symposium on Artificial Neural Networks. :91–96.
Reinhart F, Steil JJ.  2012.  Learning Whole Upper Body Control with Dynamic Redundancy Resolution in Coupled Associative Radial Basis Function Networks. IROS. :1487–1492.
Steil JJ, Krüger S.  2013.  Lernen und Sicherheit in Interaktion mit Robotern aus Maschinensicht. Robotik und Gesetzgebung. 2:51–71.
Shareef Z, Ahmed A.  2011.  LMI BASED Anti-Windup Controller Designing for Ball and Beam Control System. International Bhurban Conference on Applied Sciences and Technology.
Steil JJ.  2000.  Local input-output stability of recurrent networks with time-varying weights. Proc. European Symposium Artificial Neural Networks. :281–286.
Steil JJ.  2002.  Local structural stability of recurrent networks with time-varying weights. Neurocomputing. 48:39–51.
Ritter H, Haschke R, Röthling F, Steil JJ.  2011.  Manual Intelligence as a Rosetta Stone for Robot Cognition. Robotics Research. 66:135–146.
Rolf M, Steil JJ, Gienger M.  2010.  Mastering Growth while Bootstrapping Sensorimotor Coordination. Int. Conf. on Epigenetic Robotics.
Haus J.N, Muxfeldt A., Kubus D..  2016.  Material comparison and design of low cost modular tactile surface sensors for industrial manipulators. 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).
Steil JJ, Ritter H.  1999.  Maximisation of stability ranges for recurrent neural networks subject to on-line adaptation. Proc. European Symposium Artificial Neural Networks. :369–374.