Roberto Calandra - Bayesian optimization for robotics скачать в хорошем качестве

Roberto Calandra - Bayesian optimization for robotics

Abstract: Bayesian optimization is a popular algorithm for optimizing low-dimensional functions in a data-efficient manner. In this talk, I will discuss my practical experience with Bayesian optimization when applied to robotic applications. Along this journey, I will introduce several interesting real-world problem settings that I have encountered, and the corresponding algorithms designed as a result, as well as the new insights gained. To conclude, I will briefly discuss some future challenges and potential directions. Link to the slides: https://slides.com/rcalandra/bayesian... Papers discussed in the talk: Calandra, R.; Seyfarth, A.; Peters, J. & Deisenroth, M. P. Bayesian Optimization for Learning Gaits under Uncertainty Annals of Mathematics and Artificial Intelligence (AMAI), 2015, 76, 5-23 Yi, Z.; Calandra, R.; Veiga, F. F.; van Hoof, H.; Hermans, T.; Zhang, Y. & Peters, J. Active Tactile Object Exploration with Gaussian Processes IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, 4925-4930 Calandra, R.; Peters, J. & Deisenroth, M. P. Pareto Front Modeling for Sensitivity Analysis in Multi-Objective Bayesian Optimization NIPS Workshop on Bayesian Optimization (BayesOpt), 2014 Bansal, S.; Calandra, R.; Xiao, T.; Levine, S. & Tomlin, C. J. Goal-Driven Dynamics Learning via Bayesian Optimization IEEE Conference on Decision and Control (CDC), 2017, 5168-5173 Liao, T.; Wang, G.; Yang, B.; Lee, R.; Pister, K.; Levine, S. & Calandra, R. Data-efficient Learning of Morphology and Controller for a Microrobot IEEE International Conference on Robotics and Automation (ICRA), 2019, 2488-2494 Yang, B.; Wang, G.; Calandra, R.; Contreras, D.; Levine, S. & Pister, K. Learning Flexible and Reusable Locomotion Primitives for a Microrobot IEEE Robotics and Automation Letters (RA-L), 2018, 3, 1904-1911 Letham, B.; Calandra, R.; Rai, A. & Bakshy, E. Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization Advances in Neural Information Processing Systems (NeurIPS), 2020 Speaker: Dr. Roberto Calandra is a a Research Scientist at Facebook AI Research. Personal website can be found at https://www.robertocalandra.com/about/. This talk was given at Secondmind Labs, as a part of our (virtual) research seminar. Our research seminar is where we exchange ideas with guest speakers, keeping you up to date with the latest developments and inspiring research topics. Occasionally, Secondmind researchers present their own work as well. You can find a complete list of speakers at https://www.secondmind.ai/labs/seminars/. Learn more about Secondmind Labs at https://www.secondmind.ai/labs/​