Joschka Boedecker
Joschka Boedecker
Assistant Professor of Computer Science, University of Freiburg, Germany
Verified email at informatik.uni-freiburg.de - Homepage
Title
Cited by
Cited by
Year
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, 2746-2754, 2015
4162015
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
150*
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
1322017
Neural slam: Learning to explore with external memory
J Zhang, L Tai, J Boedecker, W Burgard, M Liu
arXiv preprint arXiv:1706.09520, 2017
732017
Uncertainty-driven imagination for continuous deep reinforcement learning
G Kalweit, J Boedecker
Conference on Robot Learning, 195-206, 2017
592017
Simspark–concepts and application in the robocup 3d soccer simulation league
J Boedecker, M Asada
Autonomous Robots 174, 181, 2008
532008
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
51*2015
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
492014
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
452009
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
442019
Real-time inverse dynamics learning for musculoskeletal robots based on echo state gaussian process regression
C Hartmann, J Boedecker, O Obst, S Ikemoto, M Asada
Proceedings of Robotics: Science and Systems VIII, 2012
312012
Flexible coordination of multiagent team behavior using HTN planning
O Obst, J Boedecker
Robot Soccer World Cup, 521-528, 2005
302005
3D2Real: Simulation league finals in real robots
N Mayer, J Boedecker, R da Silva Guerra, O Obst, M Asada
RoboCup 2006: Robot Soccer World Cup X, 25-34, 2007
292007
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
272018
Acting thoughts: Towards a mobile robotic service assistant for users with limited communication skills
F Burget, LDJ Fiederer, D Kuhner, M Völker, J Aldinger, RT Schirrmeister, ...
2017 European Conference on Mobile Robots (ECMR), 1-6, 2017
262017
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
262016
Predicting Time Series with Space-Time Convolutional and Recurrent Neural Networks.
W Groß, S Lange, J Bödecker, M Blum
ESANN, 2017
252017
Studies on reservoir initialization and dynamics shaping in echo state networks.
J Boedecker, O Obst, NM Mayer, M Asada
ESANN, 2009
192009
Simspark user’s manual
J Boedecker, K Dorer, M Rollmann, Y Xu, F Xue, M Buchta, H Vatankhah
Version 1, 17-18, 2010
182010
Getting closer: How Simulation and Humanoid League can benefit from each other
J Boedecker, NM Mayer, M Ogino, R da Silva Guerra, M Kikuchi, M Asada
Proceedings of the 3rd International Symposium on Autonomous Minirobots for …, 2006
182006
The system can't perform the operation now. Try again later.
Articles 1–20