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
Dominik BaumannAalto University, Espoo, FinlandVerified email at aalto.fi
Jeannette BohgAssistant Professor, Stanford UniversityVerified email at stanford.edu
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 BerkenkampIPAI Aleph Alpha ResearchVerified email at aleph-alpha-ip.ai
Alexander von RohrTU MunichVerified email at tum.de
Christian DanielBosch Center for Artificial IntelligenceVerified email at ias.tu-darmstadt.de
Claire TomlinUC BerkeleyVerified email at berkeley.edu
Matteo TurchettaPhD at ETH ZurichVerified email at inf.ethz.ch
Ludovic RighettiNew York University and Artificial and Natural Intelligence Toulouse InstituteVerified email at nyu.edu
Majid KhadivAssistant Professor, TUMVerified email at tum.de
Stefano PalagiScuola Superiore Sant'Anna, PisaVerified email at santannapisa.it
Shankar A. DekaAssistant Professor, Aalto UniversityVerified email at berkeley.edu
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
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	211	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	172	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	131	2019
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	49	2017
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	47	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	42	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	35	2021
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	34	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	24	2018
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	18	2022
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	15	2020
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
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 Universitat Politecnica 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	1	2023
Bayesian Optimization in Robot Learning-Automatic Controller Tuning and Sample-Efficient Methods A Marco-Valle Universität Tübingen, 2020	1	2020
Gaussian process optimization for self-tuning control A Marco Valle Universitat Politècnica de Catalunya, 2015		2015
Learning Robot Controllers under Unknown Failure Penalties using Bayesian Optimization A Marco, S Trimpe

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