Biblio

Found 205 results
Author Title [ Type(Desc)] Year
Search results for Steil
Filters: Author is Steil, Jochen J.  [Reset Search]
Conference Paper
Aswolinskiy W, Reinhart F, Steil JJ.  2016.  Time Series Classification in Reservoir- and Model-Space: A Comparison. Proc. 7th IAPR Workshop on Artificial Neural Networks in Pattern Recognition.
Shareef Z, Steil JJ.  2016.  Trajectory Optimization of COmpliant HuMANoid (COMAN) Robot Arm using Path Parameter based Dynamic Programming. Proc. IEEE Humanoids. :705–710.
Steil JJ, Kõiva R, Sperduti A.  2006.  Unsupervised Clustering of Continuous Trajectories of Kinematic Trees with SOM-SD. Proc. European Symposium on Artificial Neural Networks.
Muxfeldt A, Gopinathan S, Coenders T, Steil JJ.  2017.  A User Study on Human-Robot-Interactive Recovery for Industrial Assembly Problems. 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), p. 824-830.
Gopinathan S, Ötting S, Steil JJ.  2017.  A User Study on Personalized Adaptive Stiffness Control Modes for Human-Robot Interaction. 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), p 831-837.
Narioka K, Steil JJ.  2015.  U-shaped motor development emerges from Goal Babbling with intrinsic motor noise. :55–62.
Soltoggio A, Lemme A, Steil JJ.  2012.  Using movement primitives in interpreting and decomposing complex trajectories in learning-by-doing. :1427–1433.
Wrede S, Beyer O, Dreyer C, Wojtynek M, Steil JJ.  2016.  Vertical Integration and Service Orchestration for Modular Production Systems using Business Process Models. Procedia Technologica. 26:259–266.
Schiller U.D, Steil JJ.  2003.  On the weight dynamcis of recurrent learning. Proc. European Symposium Artificial Neural Networks. :73–78.
Steil JJ.  2011.  What do humanoid robots offer to experimental psychology ? Connectionist models of neurocognition and emergent behavior : from theory to applications ; proceedings of the 12th Neural Computation and Psychology Workshop, Birkbeck, University of London, 8 - 10 April 2010. 20:361–371.
Ötting S, Gopinathan S, Maier GW, Steil JJ.  2017.  Why Criteria of Decision Fairness Should be Considered in Robot Design. Workshop Robots in Groups and Teams at ACM Conference on Computer Supported Cooperative Work and Social Computing.
Journal Article
Steil JJ, Götting M, Wersing H, Körner E, Ritter H.  2007.  Adaptive scene dependent filters for segmentation and online learning of visual objects. Neurocomputing. 70:1235–1246.
Liu W, Chen D, Steil JJ.  2017.  Analytical Inverse Kinematics Solver for Anthropomorphic 7-DOF Redundant Manipulators with Human-Like Configuration Constraints. Journal of Intelligent & Robotic Systems. 86(1):63-79.
Schiller UD, Steil JJ.  2005.  Analyzing the weight dynamics of recurrent learning algorithms. Neurocomputing. 63:5–23.
Queißer J, Steil JJ.  2018.  Bootstrapping of Parameterized Skills Through Hybrid Optimization in Task and Policy Spaces. Frontiers in Robotics and AI, section Humanoid Robotics. 5
Wersing H, Steil JJ, Ritter H.  2001.  A Competitive Layer Model for Feature Binding and Sensory Segmentation. Neural Computation. 13:357–387.
Mohammadi P, Hoffmann EMingo, Dehio N, Malekzadeh M, Giese M, Tsagarakis N, Steil JJ.  2020.  Compliant Humanoids Moving Toward Rehabilitation Applications. Robotics and Automation Magazin.
Reinhart F, Steil JJ.  2011.  A constrained regularization approach for input-driven recurrent neural networks. Differential Equations and Dynamical Systems. 19:27–46.
He H, Chawla N, Chen H, Choe Y, Engelbrecht A, Deva J, Long L, Minai A, Nie F, Ozertem U et al..  2016.  Editorial IEEE Transactions on Neural Networks and Learning Systems 2016 and Beyond. IEEE Transactions on Neural Networks and Learning Systems. 27:1–7.
Rolf M, Steil JJ.  2014.  Efficient exploratory learning of inverse kinematics on a bionic elephant trunk. IEEE Trans. Neural Networks and Learning Systems. 25:1147–1160.
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.
Rolf M, Steil JJ.  2013.  Explorative learning of right inverse functions: theoretical implications of redundancy. Neurocomputing. 131:2-14.
Rolf M, Steil JJ, Gienger M..  2010.  Goal Babbling permits direct learning of inverse kinematics. IEEE Trans. Autonomous Mental Development. 2:216–229.
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.
Soltoggio A, Steil JJ.  2012.  How Rich Motor Skills Empower Robots at Last: Insights and Progress of the AMARSi Project. KI- Künstliche Intelligenz. 26:407–410.

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