Marc Toussaint

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Danny DriessTU BerlinVerified email at ipvs.uni-stuttgart.de
Sethu VijayakumarProfessor of Robotics, University of Edinburgh; Programme Director at The Alan Turing Institute, UKVerified email at ed.ac.uk
Christian IgelDepartment of Computer Science, University of CopenhagenVerified email at diku.dk
Manuel LopesInstituto Superior Tecnico, INESC-IDVerified email at tecnico.ulisboa.pt
Nikolay JetchevSenior Research Scientist at ZalandoVerified email at zalando.de
Ozgur S. OguzUni Stuttgart & MPI ISVerified email at ipvs.uni-stuttgart.de
Michael GiengerHonda Research Institute EuropeVerified email at honda-ri.de
Peter EnglertRealtime RoboticsVerified email at rtr.ai
Andreas OrtheyRealtime RoboticsVerified email at rtr.ai
Amos StorkeyProfessor of Machine Learning and AI, School of Informatics, University of Edinburgh, UKVerified email at ed.ac.uk
Ngo Anh VienBosch Center for Artificial Intelligence (BCAI)Verified email at bosch.com
Stefan SchaalRobotics, Machine Learning, AI, NeuroscienceVerified email at google.com
Jim MainpriceMercedes-Benz AG R&D - Automated DrivingVerified email at mercedes-benz.com
Jung-Su HaLearning & Intelligent Systems, TU Berlin, Max Planck Institute for Intelligent SystemsVerified email at tu-berlin.de
Duy Nguyen-TuongBosch Center for AI, GermanyVerified email at robot-learning.de
Johannes KulickApplied Scientist at Amazon BerlinVerified email at amazon.com
Konrad RawlikPostdoctoral Fellow, Roslin Institute, University of EdinburghVerified email at roslin.ed.ac.uk
Shlomo ZilbersteinProfessor of Computer Science, University of Massachusetts AmherstVerified email at cs.umass.edu
Nathan RatliffNVIDIAVerified email at nvidia.com
Akshat KumarAssociate Professor, School of Information Systems, Singapore ManagementVerified email at smu.edu.sg

Marc Toussaint

Professor of Computer Science, TU Berlin, Germany

Verified email at tu-berlin.de - Homepage

Robotics Robot Learning Artificial Intelligence


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Using machine learning to focus iterative optimization F Agakov, E Bonilla, J Cavazos, B Franke, G Fursin, MFP O'Boyle, ... International Symposium on Code Generation and Optimization (CGO'06), 11 pp.-305, 2006	485	2006
Robot trajectory optimization using approximate inference M Toussaint Proceedings of the 26th annual international conference on machine learning …, 2009	333	2009
On stochastic optimal control and reinforcement learning by approximate inference K Rawlik, M Toussaint, S Vijayakumar Proceedings of Robotics: Science and Systems VIII, 2012	265	2012
Planning as inference M Botvinick, M Toussaint Trends in cognitive sciences 16 (10), 485-488, 2012	264	2012
Probabilistic inference for solving discrete and continuous state Markov Decision Processes M Toussaint, A Storkey Proceedings of the 23rd international conference on Machine learning, 945-952, 2006	244	2006
Differentiable physics and stable modes for tool-use and manipulation planning MA Toussaint, KR Allen, KA Smith, JB Tenenbaum Robotics: Science and Systems Foundation, 2018	208	2018
Logic-geometric programming: An optimization-based approach to combined task and motion planning M Toussaint Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015	202	2015
Exploration in model-based reinforcement learning by empirically estimating learning progress M Lopes, T Lang, M Toussaint, PY Oudeyer Advances in neural information processing systems 25, 2012	188	2012
Gaussian process implicit surfaces for shape estimation and grasping S Dragiev, M Toussaint, M Gienger 2011 IEEE International Conference on Robotics and Automation, 2845-2850, 2011	163	2011
A no-free-lunch theorem for non-uniform distributions of target functions C Igel, M Toussaint Journal of Mathematical Modelling and Algorithms 3 (4), 313-322, 2005	163	2005
Multi-class image segmentation using conditional random fields and global classification N Plath, M Toussaint, S Nakajima Proceedings of the 26th annual international conference on machine learning …, 2009	148	2009
Probabilistic inference as a model of planned behavior. M Toussaint Künstliche Intell. 23 (3), 23-29, 2009	140	2009
On classes of functions for which no free lunch results hold C Igel, M Toussaint arXiv preprint cs/0108011, 2001	127	2001
Hierarchical POMDP Controller Optimization by Likelihood Maximization. M Toussaint, L Charlin, P Poupart UAI 24, 562-570, 2008	109	2008
Probabilistic inference for solving (PO) MDPs M Toussaint, S Harmeling, A Storkey Technical Report EDI-INF-RR-0934, School of Informatics, University of Edinburgh, 2006	106	2006
Planning with noisy probabilistic relational rules T Lang, M Toussaint Journal of Artificial Intelligence Research 39, 1-49, 2010	91	2010
Active Learning for Teaching a Robot Grounded Relational Symbols. J Kulick, M Toussaint, T Lang, M Lopes IJCAI, 1451-1457, 2013	90	2013
Probabilistic recurrent state-space models A Doerr, C Daniel, M Schiegg, NT Duy, S Schaal, M Toussaint, ... International Conference on Machine Learning, 1280-1289, 2018	88	2018
Learning model-free robot control by a Monte Carlo EM algorithm N Vlassis, M Toussaint, G Kontes, S Piperidis Autonomous Robots 27 (2), 123-130, 2009	86	2009
Fast motion planning from experience: trajectory prediction for speeding up movement generation N Jetchev, M Toussaint Autonomous Robots 34 (1), 111-127, 2013	84	2013

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