Alonso Marco

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Sebastian TrimpeProfessor, RWTH Aachen UniversityVerified email at dsme.rwth-aachen.de
Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceVerified email at google.com
Philipp HennigUniversity of TübingenVerified email at uni-tuebingen.de
Jeannette BohgAssistant Professor, Stanford UniversityVerified email at stanford.edu
Dominik BaumannAalto University, Espoo, FinlandVerified email at aalto.fi
Andreas DoerrSenior Applied Scientist @ Amazon AWS, ex. Bosch Center for AI, MPI for Intelligent SystemsVerified email at tuebingen.mpg.de
Duy Nguyen-TuongBosch Center for AI, GermanyVerified email at robot-learning.de
Andreas KrauseProfessor of Computer Science, ETH ZurichVerified email at inf.ethz.ch
Angela SchoelligProfessor of Robotics, Technical University of Munich and University of TorontoVerified email at tum.de
Felix BerkenkampBosch Center for AIVerified email at de.bosch.com
Alexander von RohrRWTH Aachen UniversityVerified email at dsme.rwth-aachen.de
Christian DanielBosch Center for Artificial IntelligenceVerified email at ias.tu-darmstadt.de
Stefano PalagiScuola Superiore Sant'Anna, PisaVerified email at santannapisa.it
Ludovic RighettiNew York UniversityVerified email at nyu.edu
Majid KhadivAssistant Professor, TUMVerified email at tum.de
Claire TomlinUC BerkeleyVerified email at berkeley.edu
Matteo TurchettaPhD at ETH ZurichVerified email at inf.ethz.ch
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Shankar A. DekaPostdoc, EECS, University of California, BerkeleyVerified email at berkeley.edu
Marc ToussaintProfessor of Computer Science, TU Berlin, GermanyVerified email at tu-berlin.de

Alonso Marco

University of California Berkeley

Verified email at berkeley.edu - Homepage

Model-based learning variational inference Bayesian optimization optimal control Gaussian process


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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	166	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	142	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	88	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	41	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	40	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	30	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	19	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	18	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	14	2015
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	12	2021
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
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	6	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
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
Bayesian Optimization in Robot Learning-Automatic Controller Tuning and Sample-Efficient Methods A Marco-Valle Universität Tübingen, 2020		2020
Learning Robot Controllers under Unknown Failure Penalties using Bayesian Optimization A Marco, S Trimpe

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