| Automatic LQR tuning based on Gaussian process global optimization A Marco, P Hennig, J Bohg, S Schaal, S Trimpe 2016 IEEE international conference on robotics and automation (ICRA), 270-277, 2016 | 146 | 2016 |
| 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 | 128 | 2017 |
| 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 | 70 | 2019 |
| Optimizing long-term predictions for model-based policy search A Doerr, C Daniel, D Nguyen-Tuong, A Marco, S Schaal, T Marc, S Trimpe Conference on Robot Learning, 227-238, 2017 | 38 | 2017 |
| Model-based policy search for automatic tuning of multivariate PID controllers A Doerr, D Nguyen-Tuong, A Marco, S Schaal, S Trimpe 2017 IEEE International Conference on Robotics and Automation (ICRA), 5295-5301, 2017 | 37 | 2017 |
| On the design of LQR kernels for efficient controller learning A Marco, P Hennig, S Schaal, S Trimpe 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 5193-5200, 2017 | 26 | 2017 |
| Gait learning for soft microrobots controlled by light fields A von Rohr, S Trimpe, A Marco, P Fischer, S Palagi 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 18 | 2018 |
| Robot learning with crash constraints A Marco, D Baumann, M Khadiv, P Hennig, L Righetti, S Trimpe IEEE Robotics and Automation Letters 6 (2), 1439-1446, 2021 | 13 | 2021 |
| Automatic LQR tuning based on Gaussian process optimization: Early experimental results A Marco, P Hennig, J Bohg, S Schaal, S Trimpe Second Machine Learning in Planning and Control of Robot Motion Workshop at …, 2015 | 13 | 2015 |
| Excursion search for constrained bayesian optimization under a limited budget of failures A Marco, A von Rohr, D Baumann, JM Hernández-Lobato, S Trimpe arXiv preprint arXiv:2005.07443, 2020 | 9 | 2020 |
| Gosafe: Globally optimal safe robot learning D Baumann, A Marco, M Turchetta, S Trimpe 2021 IEEE International Conference on Robotics and Automation (ICRA), 4452-4458, 2021 | 8 | 2021 |
| Classified regression for Bayesian optimization: Robot learning with unknown penalties A Marco, D Baumann, P Hennig, S Trimpe arXiv preprint arXiv:1907.10383, 2019 | 3 | 2019 |
| Learning Robot Controllers under Unknown Failure Penalties using Bayesian Optimization A Marco, S Trimpe | | |
Sebastian TrimpeProfessor, RWTH Aachen UniversityVerified email at dsme.rwth-aachen.de
