Joschka Boedecker
Joschka Boedecker
Professor of Computer Science, University of Freiburg, Germany
Zweryfikowany adres z informatik.uni-freiburg.de - Strona główna
Cytowane przez
Cytowane przez
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
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
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
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
Machine-learning-based diagnostics of EEG pathology
LAW Gemein, RT Schirrmeister, P Chrabąszcz, D Wilson, J Boedecker, ...
NeuroImage 220, 117021, 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
Uncertainty-driven imagination for continuous deep reinforcement learning
G Kalweit, J Boedecker
Conference on robot learning, 195-206, 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
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
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
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
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
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
Simspark–concepts and application in the robocup 3d soccer simulation league
J Boedecker, M Asada
Autonomous Robots 174, 181, 2008
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
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
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
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
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
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
Nie można teraz wykonać tej operacji. Spróbuj ponownie później.
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