Uniform error bounds for Gaussian process regression with application to safe control A Lederer, J Umlauft, S Hirche Advances in Neural Information Processing Systems 32, 2019 | 182 | 2019 |
Feedback linearization based on Gaussian processes with event-triggered online learning J Umlauft, S Hirche IEEE Transactions on Automatic Control 65 (10), 4154-4169, 2019 | 140 | 2019 |
An uncertainty-based control Lyapunov approach for control-affine systems modeled by Gaussian process J Umlauft, L Pöhler, S Hirche IEEE Control Systems Letters 2 (3), 483-488, 2018 | 95 | 2018 |
Feedback linearization using Gaussian processes J Umlauft, T Beckers, M Kimmel, S Hirche 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5249-5255, 2017 | 80 | 2017 |
Learning Stable Gaussian Process State Space Models J Umlauft, A Lederer, S Hirche 2017 American Control Conference (ACC) 1, 6428-6434, 2017 | 47 | 2017 |
Gaussian process-based real-time learning for safety critical applications A Lederer, AJO Conejo, KA Maier, W Xiao, J Umlauft, S Hirche International Conference on Machine Learning, 6055-6064, 2021 | 45 | 2021 |
Scenario-based optimal control for Gaussian process state space models J Umlauft, T Beckers, S Hirche 2018 European Control Conference (ECC), 1386-1392, 2018 | 41 | 2018 |
Dynamic movement primitives for cooperative manipulation and synchronized motions J Umlauft, D Sieber, S Hirche 2014 IEEE International Conference on Robotics and Automation (ICRA), 766-771, 2014 | 39 | 2014 |
Localized active learning of Gaussian process state space models A Capone, G Noske, J Umlauft, T Beckers, A Lederer, S Hirche Learning for Dynamics and Control, 490-499, 2020 | 36 | 2020 |
Stable model-based control with Gaussian process regression for robot manipulators T Beckers, J Umlauft, S Hirche IFAC-PapersOnLine 50 (1), 3877-3884, 2017 | 33 | 2017 |
Smart forgetting for safe online learning with Gaussian processes J Umlauft, T Beckers, A Capone, A Lederer, S Hirche Learning for dynamics and control, 160-169, 2020 | 31 | 2020 |
Learning stable stochastic nonlinear dynamical systems J Umlauft, S Hirche International Conference on Machine Learning, 3502-3510, 2017 | 31 | 2017 |
Gaussian processes for dynamic movement primitives with application in knowledge-based cooperation Y Fanger, J Umlauft, S Hirche 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2016 | 31 | 2016 |
Stable Gaussian process based tracking control of Lagrangian systems T Beckers, J Umlauft, D Kulic, S Hirche 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5180-5185, 2017 | 30 | 2017 |
How training data impacts performance in learning-based control A Lederer, A Capone, J Umlauft, S Hirche IEEE Control Systems Letters 5 (3), 905-910, 2020 | 27 | 2020 |
Mean square prediction error of misspecified Gaussian process models T Beckers, J Umlauft, S Hirche 2018 IEEE Conference on Decision and Control (CDC), 1162-1167, 2018 | 26 | 2018 |
Posterior variance analysis of Gaussian processes with application to average learning curves A Lederer, J Umlauft, S Hirche arXiv preprint arXiv:1906.01404, 2019 | 21 | 2019 |
Bayesian Uncertainty Modeling for Programming by Demonstration J Umlauft, Y Fanger, S Hirche 2017 IEEE International Conference on Robotics and Automation (ICRA) 1, 6428 …, 2017 | 21 | 2017 |
Real-time regression with dividing local Gaussian processes A Lederer, AJO Conejo, K Maier, W Xiao, J Umlauft, S Hirche arXiv preprint arXiv:2006.09446, 2020 | 20 | 2020 |
Learning stochastically stable Gaussian process state–space models J Umlauft, S Hirche IFAC Journal of Systems and Control 12, 100079, 2020 | 20 | 2020 |