Seguir
Joschka Boedecker
Joschka Boedecker
Professor of Computer Science, University of Freiburg, Germany
E-mail confirmado em informatik.uni-freiburg.de - Página inicial
Título
Citado por
Citado por
Ano
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
9762015
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
324*
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
3132017
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
2542018
Machine-learning-based diagnostics of EEG pathology
LAW Gemein, RT Schirrmeister, P Chrabąszcz, D Wilson, J Boedecker, ...
NeuroImage 220, 117021, 2020
2282020
Neural slam: Learning to explore with external memory
J Zhang, L Tai, J Boedecker, W Burgard, M Liu
arXiv preprint arXiv:1706.09520, 2017
1812017
Uncertainty-driven imagination for continuous deep reinforcement learning
G Kalweit, J Boedecker
Conference on robot learning, 195-206, 2017
1652017
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
1432019
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
1382020
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
1052014
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
1012016
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
912019
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
892015
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
792023
Simspark–concepts and application in the robocup 3d soccer simulation league
J Boedecker, M Asada
Autonomous Robots 174, 181, 2008
722008
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
562019
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
562018
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
53
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
472009
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
452020
O sistema não pode executar a operação agora. Tente novamente mais tarde.
Artigos 1–20