Embed to control: A locally linear latent dynamics model for control from raw images M Watter, J Springenberg, J Boedecker, M Riedmiller Advances in neural information processing systems 28, 2015 | 898 | 2015 |
Information Processing in Echo State Networks at the Edge of Chaos MA Joschka Boedecker, Oliver Obst, Joseph T. Lizier Theory in Biosciences 131 (3), 205-213, 0 | 296* | |
Deep reinforcement learning with successor features for navigation across similar environments J Zhang, JT Springenberg, J Boedecker, W Burgard 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 289 | 2017 |
High-level decision making for safe and reasonable autonomous lane changing using reinforcement learning B Mirchevska, C Pek, M Werling, M Althoff, J Boedecker 2018 21st International Conference on Intelligent Transportation Systems …, 2018 | 211 | 2018 |
Machine-learning-based diagnostics of EEG pathology LAW Gemein, RT Schirrmeister, P Chrabąszcz, D Wilson, J Boedecker, ... NeuroImage 220, 117021, 2020 | 188 | 2020 |
Neural slam: Learning to explore with external memory J Zhang, L Tai, J Boedecker, W Burgard, M Liu arXiv preprint arXiv:1706.09520, 2017 | 171 | 2017 |
Uncertainty-driven imagination for continuous deep reinforcement learning G Kalweit, J Boedecker Conference on robot learning, 195-206, 2017 | 148 | 2017 |
Vr-goggles for robots: Real-to-sim domain adaptation for visual control J Zhang, L Tai, P Yun, Y Xiong, M Liu, J Boedecker, W Burgard IEEE Robotics and Automation Letters 4 (2), 1148-1155, 2019 | 125 | 2019 |
Applied machine learning and artificial intelligence in rheumatology M Hügle, P Omoumi, JM van Laar, J Boedecker, T Hügle Rheumatology advances in practice 4 (1), rkaa005, 2020 | 111 | 2020 |
Approximate real-time optimal control based on sparse gaussian process models J Boedecker, JT Springenberg, J Wülfing, M Riedmiller 2014 IEEE symposium on adaptive dynamic programming and reinforcement …, 2014 | 101 | 2014 |
A survey of deep network solutions for learning control in robotics: From reinforcement to imitation L Tai, J Zhang, M Liu, J Boedecker, W Burgard arXiv preprint arXiv:1612.07139, 2016 | 97 | 2016 |
Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real … W Böhmer, JT Springenberg, J Boedecker, M Riedmiller, K Obermayer KI-Künstliche Intelligenz 29 (4), 353-362, 2015 | 87 | 2015 |
Dynamic input for deep reinforcement learning in autonomous driving M Huegle, G Kalweit, B Mirchevska, M Werling, J Boedecker 2019 IEEE/RSJ international conference on intelligent robots and systems …, 2019 | 71 | 2019 |
Simspark–concepts and application in the robocup 3d soccer simulation league J Boedecker, M Asada Autonomous Robots 174, 181, 2008 | 70 | 2008 |
Latent plans for task-agnostic offline reinforcement learning E Rosete-Beas, O Mees, G Kalweit, J Boedecker, W Burgard Conference on Robot Learning, 1838-1849, 2023 | 48 | 2023 |
Early seizure detection with an energy-efficient convolutional neural network on an implantable microcontroller M Hügle, S Heller, M Watter, M Blum, F Manzouri, M Dumpelmann, ... 2018 International Joint Conference on Neural Networks (IJCNN), 1-7, 2018 | 47 | 2018 |
Initialization and self‐organized optimization of recurrent neural network connectivity J Boedecker, O Obst, NM Mayer, M Asada HFSP journal 3 (5), 340-349, 2009 | 47 | 2009 |
A service assistant combining autonomous robotics, flexible goal formulation, and deep-learning-based brain–computer interfacing D Kuhner, LDJ Fiederer, J Aldinger, F Burget, M Völker, RT Schirrmeister, ... Robotics and Autonomous Systems 116, 98-113, 2019 | 45 | 2019 |
Deep reinforcement learning with successor features for navigation across similar environments. In 2017 IEEE J Zhang, JT Springenberg, J Boedecker, W Burgard RSJ International Conference on Intelligent Robots and Systems (IROS), 2371-2378, 0 | 43 | |
Dynamic interaction-aware scene understanding for reinforcement learning in autonomous driving M Hügle, G Kalweit, M Werling, J Boedecker 2020 IEEE international conference on robotics and automation (ICRA), 4329-4335, 2020 | 39 | 2020 |