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 | 190 | 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 | 160 | 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 | 106 | 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 | 46 | 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 | 45 | 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 | 32 | 2017 |
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 | 30 | 2021 |
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 | 28 | 2021 |
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 | 23 | 2018 |
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 | 12 | 2020 |
Koopman-based neural lyapunov functions for general attractors SA Deka, AM Valle, CJ Tomlin 2022 IEEE 61st Conference on Decision and Control (CDC), 5123-5128, 2022 | 9 | 2022 |
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 |
Gaussian process optimization for self-tuning control A Marco Valle Universitat Politècnica de Catalunya, 2015 | 2 | 2015 |
Bayesian Optimization in Robot Learning-Automatic Controller Tuning and Sample-Efficient Methods A Marco-Valle Universität Tübingen, 2020 | 1 | 2020 |
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models A Marco, E Morley, CJ Tomlin 2023 IEEE 62nd Conference on Decision and Control (CDC), 2023 | | 2023 |
Learning Robot Controllers under Unknown Failure Penalties using Bayesian Optimization A Marco, S Trimpe | | |