| Event-based state estimation with variance-based triggering S Trimpe, R D'Andrea IEEE Transactions on Automatic Control 59 (12), 3266-3281, 2014 | 273 | 2014 |
| Event-based state estimation with variance-based triggering S Trimpe, R D'Andrea Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, 6583-6590, 2012 | 273 | 2012 |
| Learning an approximate model predictive controller with guarantees M Hertneck, J Köhler, S Trimpe, F Allgöwer IEEE Control Systems Letters 2 (3), 543-548, 2018 | 241 | 2018 |
| Automatic LQR tuning based on Gaussian process global optimization A Marco, P Hennig, J Bohg, S Schaal, S Trimpe Robotics and Automation (ICRA), 2016 IEEE International Conference on, 270-277, 2016 | 187 | 2016 |
| Safe and fast tracking on a robot manipulator: Robust mpc and neural network control J Nubert, J Köhler, V Berenz, F Allgöwer, S Trimpe IEEE Robotics and Automation Letters 5 (2), 3050-3057, 2020 | 161 | 2020 |
| Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization A Marco, F Berkenkamp, P Hennig, AP Schoellig, A Krause, S Schaal, ... 2017 IEEE International Conference on Robotics and Automation (ICRA), 1557-1563, 2017 | 157 | 2017 |
| Probabilistic recurrent state-space models A Doerr, C Daniel, M Schiegg, D Nguyen-Tuong, S Schaal, M Toussaint, ... International Conference on Machine Learning (ICML) 80, 1280-1289, 2018 | 136 | 2018 |
| Data-efficient autotuning with bayesian optimization: An industrial control study M Neumann-Brosig, A Marco, D Schwarzmann, S Trimpe IEEE Transactions on Control Systems Technology 28 (3), 730-740, 2019 | 100 | 2019 |
| Learning-based robust model predictive control with state-dependent uncertainty R Soloperto, MA Müller, S Trimpe, F Allgöwer IFAC-PapersOnLine 51 (20), 442-447, 2018 | 100 | 2018 |
| Depth-based object tracking using a robust gaussian filter J Issac, M Wüthrich, CG Cifuentes, J Bohg, S Trimpe, S Schaal 2016 IEEE international conference on robotics and automation (ICRA), 608-615, 2016 | 87 | 2016 |
| An experimental demonstration of a distributed and event-based state estimation algorithm S Trimpe, R D'Andrea IFAC Proceedings Volumes 44 (1), 8811-8818, 2011 | 83 | 2011 |
| Actively learning gaussian process dynamics M Buisson-Fenet, F Solowjow, S Trimpe Learning for dynamics and control, 5-15, 2020 | 77 | 2020 |
| Feedback control goes wireless: Guaranteed stability over low-power multi-hop networks F Mager, D Baumann, R Jacob, L Thiele, S Trimpe, M Zimmerling Proceedings of the 10th ACM/IEEE International Conference on Cyber-Physical …, 2019 | 73 | 2019 |
| Deep reinforcement learning for event-triggered control D Baumann, JJ Zhu, G Martius, S Trimpe 2018 IEEE Conference on Decision and Control (CDC), 943-950, 2018 | 65 | 2018 |
| A Self-Tuning LQR Approach Demonstrated on an Inverted Pendulum S Trimpe, A Millane, S Doessegger, R D’Andrea Proc. of the 19th IFAC World Congress, 2014 | 64 | 2014 |
| Accelerometer-based tilt estimation of a rigid body with only rotational degrees of freedom S Trimpe, R D'Andrea 2010 IEEE International Conference on Robotics and Automation, 2630-2636, 2010 | 63 | 2010 |
| Practical and rigorous uncertainty bounds for gaussian process regression C Fiedler, CW Scherer, S Trimpe Proceedings of the AAAI conference on artificial intelligence 35 (8), 7439-7447, 2021 | 61 | 2021 |
| Distributed event-based state estimation for networked systems: An LMI approach M Muehlebach, S Trimpe IEEE Transactions on Automatic Control 63 (1), 269-276, 2017 | 59 | 2017 |
| Wireless control for smart manufacturing: Recent approaches and open challenges D Baumann, F Mager, U Wetzker, L Thiele, M Zimmerling, S Trimpe Proceedings of the IEEE 109 (4), 441-467, 2020 | 54 | 2020 |
| Sliding mode control with gaussian process regression for underwater robots GS Lima, S Trimpe, WM Bessa Journal of Intelligent & Robotic Systems 99 (3), 487-498, 2020 | 48 | 2020 |
Dominik BaumannAalto University, Espoo, Finland在 aalto.fi 的电子邮件经过验证
