Philip Becker-Ehmck

Cited by

	All	Since 2020
Citations	196	180
h-index	5	5
i10-index	3	3

201820192020202120222023202420255 11 28 49 35 24 39 5

Co-authors

Patrick van der SmagtELTE University | Foundation RoboticsVerified email at tum.de
Maximilian KarlResearch ScientistVerified email at in.tum.de
Jan PetersProfessor for Intelligent Autonomous Systems/TU Darmstadt, Dept. Head/German AI Research Center DFKIVerified email at ias.tu-darmstadt.de
Justin BayerVolkswagen AGVerified email at volkswagen.de
Djalel BenbouzidMachine Learnig Research Lab, Volkswagen GroupVerified email at argmax.ai
Maximilian SoelchVolkswagen Group Data & AIVerified email at volkswagen.de
Xingyuan ZhangMachine Learning Research Lab, Volkswagen AG; Technical University of MunichVerified email at tum.de
Neha DasDoctoral Candidate, Technical University of MunichVerified email at tum.de
Marvin AllesVolkswagen Group, Technical University of MunichVerified email at tum.de

Philip Becker-Ehmck

Volkswagen Group

Verified email at volkswagen.de

Artificial Intelligence Deep Learning Reinforcement Learning Generative AI


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Switching Linear Dynamics for Variational Bayes Filtering P Becker-Ehmck, J Peters, P van der Smagt 36th International Conference on Machine Learning (ICML), 2018	65	2018
Unsupervised real-time control through variational empowerment M Karl, P Becker-Ehmck, M Soelch, D Benbouzid, P van der Smagt, ... The International Symposium of Robotics Research, 158-173, 2019	62	2019
Learning to Fly via Deep Model-Based Reinforcement Learning P Becker-Ehmck, M Karl, J Peters, P van der Smagt arXiv preprint arXiv:2003.08876, 2020	52	2020
Action Inference by Maximising Evidence: Zero-Shot Imitation from Observation with World Models X Zhang, P Becker-Ehmck, P van der Smagt, M Karl Thirty-Seventh Conference on Advances in Neural Information Processing Systems, 2023	6	2023
Exploration via Empowerment Gain: Combining Novelty, Surprise and Learning Progress P Becker-Ehmck, M Karl, J Peters, P van der Smagt ICML 2021 Workshop on Unsupervised Reinforcement Learning, 2021	6	2021
Beta DVBF: Learning State-Space Models for Control from High Dimensional Observations N Das, M Karl, P Becker-Ehmck, P van der Smagt arXiv preprint arXiv:1911.00756, 2019	5	2019
Constrained Latent Action Policies for Model-Based Offline Reinforcement Learning M Alles, P Becker-Ehmck, P van der Smagt, M Karl Advances in Neural Information Processing Systems 37, 70381-70405, 2024		2024
Overcoming Knowledge Barriers: Online Imitation Learning from Observation with Pretrained World Models X Zhang, P Becker-Ehmck, P van der Smagt, M Karl arXiv preprint arXiv:2404.18896, 2024		2024
Latent State-Space Models for Control P Becker-Ehmck Technische Universität Darmstadt, 2022		2022

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