Biblio

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Search results for Steil
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Steil JJ, Maier GW.  2020.  Kollaborative Roboter – universale Werkzeuge in der digitalisierten und vernetzten Arbeitswelt. G. W. Maier, G. Engels, E. Steffen (Hrg.): Handbuch Gestaltung digitaler und vernetzter Arbeitswelten. :323-346.
Steil JJ.  2021.  "Können Maschinen Ethisches Verhalten Lernen ?" Bericht zum 1. SYnENZ Zirkel der BWG Kommission für Synergie und Intelligenz (SYnENZ) Jahrbuch der Braunschweigischen Wissenschaftlichen Gesellschaft. :117-120.
Huchler N, Adolph L, André E, Bauer W, Bender N, Müller N, Neuburger R, Peissner M, Steil JJ, Stowasser S et al..  2020.  Kriterien für die menschengerechte Gestaltung der Mensch-Maschine-Interaktion bei Lernenden Systemen – Whitepaper aus der Plattform Lernende Systeme.
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Wersing H, Steil JJ, Ritter H.  1997.  A Layered Recurrent Neural Network for Feature Grouping. Int. Conf. on Artificial Neural Networks. :439–444.
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.  2018.  Learning Forward and Inverse Kinematics Maps Efficiently. IROS 2018.
Malekzadeh M, Queißer J, Steil JJ.  2015.  Learning from demonstration for Bionic Handling Assistant robot.
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.
Rayyes R, Kubus D, Steil JJ.  2018.  Learning Inverse Statics Models Efficiently with Symmetry-Based Exploration. Frontiers in Neurorobotics.
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.
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.
Ötting S, Masjutin L, Steil JJ, Maier GW.  2020.  Let's Work Together: A Meta-Analysis on Robot Design Features that Enable Successful Human–Robot Interaction at Work. Human Factors.
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.
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Ritter H, Haschke R, Röthling F, Steil JJ.  2011.  Manual Intelligence as a Rosetta Stone for Robot Cognition. Robotics Research. 66:135–146.
Reinhart F, Neumann K, Aswolinskiy W, Steil JJ, Hammer B.  2018.  Maschinelles Lernen in technischen Systemen. Steigerung der Intelligenz mechatronischer Systeme. :pp.73-118.

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