PUB - Publikationen an der Universität Bielefeld

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• [16]
  

  2024 | Preprint | Eingereicht | PUB-ID: 2994268

  L. Bergmann, et al., “Precision-Focused Reinforcement Learning Model for Robotic Object Pushing”, Submitted.

  PUB | DOI | Download (ext.)
   
• [15]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710097

  M. Rolf, et al., “A Multi-Level Control Architecture for the Bionic Handling Assistant”, Advanced Robotics, vol. 29, 2015, pp. 847-859.

  PUB | PDF | DOI | WoS
   
• [14]
  

  2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2729562

  K. Neumann and J.J. Steil, “Learning Robot Motions with Stable Dynamical Systems under Diffeomorphic Transformations”, Robotics and Autonomous Systems, vol. 70, 2015, pp. 1-15.

  PUB | PDF | DOI | WoS
   
• [13]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2685050

  J. Queißer, et al., “An Active Compliant Control Mode for Interaction with a Pneumatic Soft Robot”, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), IEEE, 2014, pp.573-579.

  PUB | PDF | DOI
   
• [12]
  

  2014 | Bielefelder E-Dissertation | PUB-ID: 2656403

  K. Neumann, Reliability of Extreme Learning Machines, Bielefeld: Bielefeld University Library, 2014.

  PUB | PDF
   
• [11]
  

  2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2656292

  A. Lemme, et al., “Neural Learning of Vector Fields for Encoding Stable Dynamical Systems”, Neurocomputing, vol. 141, 2014, pp. 3-14.

  PUB | PDF | DOI | WoS
   
• [10]
  

  2014 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2900710

  A. Unger, et al., “Modeling the Ultrasonic Softening Effect for Robust Copper Wire Bonding”, 8th International Conference on Integrated Power Electronics Systems, Proceedings, February, 25-27, 2014 Nuremberg/Germany, ETG-Fachbericht, vol. 141, VDE-Verlag, 2014.

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• [9]
  

  2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2581205

  A. Lemme, et al., “Neurally imprinted stable vector fields”, Presented at the European Symposium on Artificial Neural Networks, (best paper award), 2013.

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• [8]
  

  2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2637630

  K. Neumann, A. Lemme, and J.J. Steil, “Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates”, Presented at the Int. Conference Intelligent Robotics and Systems, Tokio, IEEE, 2013.

  PUB | PDF
   
• [7]
  

  2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2547891

  K. Neumann, C. Strub, and J.J. Steil, “Intrinsic Plasticity via Natural Gradient Descent with Application to Drift Compensation”, Neurocomputing, vol. 112, 2013, pp. 26-33.

  PUB | PDF | DOI | WoS
   
• [6]
  

  2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2547909

  K. Neumann, M. Rolf, and J.J. Steil, “Reliable Integration of Continuous Constraints into Extreme Learning Machines”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 21, 2013, pp. 35-50.

  PUB | PDF | DOI | WoS
   
• [5]
  

  2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2465823

  K. Neumann and J.J. Steil, “Optimizing Extreme Learning Machines via Ridge Regression and Batch Intrinsic Plasticity”, Neurocomputing, vol. 102, 2013, pp. 23-30.

  PUB | PDF | DOI | WoS
   
• [4]
  

  2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2508614

  K. Neumann, C. Emmerich, and J.J. Steil, “Regularization by Intrinsic Plasticity and its Synergies with Recurrence for Random Projection Methods”, Journal of Intelligent Learning Systems and Applications, vol. 4, 2012, pp. 230-246.

  PUB | PDF | DOI
   
• [3]
  

  2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2465819

  K. Neumann and J.J. Steil, “Intrinsic Plasticity via Natural Gradient Decent”, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012). Proceedings, M. Verleysen, ed., Louvain-la-Neuve: Ciaco, 2012, pp.555-560.

  PUB | PDF
   
• [2]
  

  2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2141968

  K. Neumann and J.J. Steil, “Batch Intrinsic Plasticity for Extreme Learning Machines”, Artificial Neural Networks and Machine Learning – ICANN 2011. Pt. 1, T. Honkela, et al., eds., Lecture notes in computer science, vol. 6791, Springer, 2011, pp.339-346.

  PUB | PDF | DOI
   
• [1]
  

  2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2142025

  K. Neumann, et al., “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, S. Doncieux, et al., eds., Lecture notes in computer science, vol. 6226, Springer, 2010.

  PUB | PDF | DOI