LEARNING ROBOTS / ROBOT LEARNING (original) (raw)

Selected Publications on Robot Learning / Soccer Learning

34. M. Stollenga, L. Pape, M. Frank, J. Leitner, A. Förster, J. Schmidhuber. Task-Relevant Roadmaps: A Framework for Humanoid Motion Planning. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, 2013.PDF.

33. H. Ngo, M. Luciw, A. Förster, J. Schmidhuber.Confidence-based progress-driven self-generated goals for skill acquisition in developmental robots. Frontiers in Psychology, 2013. doi: 10.3389/fpsyg.2013.00833

32. J. Leitner, S. Harding, M. Frank, A. Förster, J. Schmidhuber. Artificial Neural Networks For Spatial Perception: Towards Visual Object Localisation in Humanoid Robots. International Joint Conference on Neural Networks (IJCNN), Dallas, USA, 2013.PDF.

31. J. Leitner, S. Harding, M. Frank, A. Förster, J. Schmidhuber. Humanoid Learns to Detect Its Own Hands. IEEE Congress on Evolutionary Computing (CEC), Cancun, Mexico, 2013.PDF.

30. J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez. Evolving Large-Scale Neural Networks for Vision-Based Reinforcement Learning. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Amsterdam, 2013.PDF.

29. M. Frank, J. Leitner, M. Stollenga, G. Kaufmann, S. Harding, A. F�rster, J. Schmidhuber. The Modular Behavioral Environment for Humanoids & other Robots (MoBeE).9th International Conference on Informatics in Control, Automation and Robotics (ICINCO). Rome, Italy, 2012. PDF.

28. V. R. Kompella, M. Luciw, M. Stollenga, L. Pape, J. Schmidhuber. Autonomous Learning of Abstractions using Curiosity-Driven Modular Incremental Slow Feature Analysis. Proc. IEEE Conference on Development and Learning / EpiRob 2012(ICDL-EpiRob'12), San Diego, 2012.

27. J. Leitner, S. Harding, M. Frank, A. Foerster, J. Schmidhuber. Transferring Spatial Perception Between Robots Operating In A Shared Workspace. Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'12), Vilamoura, 2012.

26. J. Leitner, P. Chandrashekhariah, S. Harding, M. Frank, G. Spina, A. Foerster, J. Triesch, J. Schmidhuber. Autonomous Learning Of Robust Visual Object Detection And Identification On A Humanoid. Proc. IEEE Conference on Development and Learning / EpiRob 2012(ICDL-EpiRob'12), San Diego, 2012.

25. L. Pape, C. M. Oddo, M. Controzzi, C. Cipriani, A. Foerster, M. C. Carrozza, J. Schmidhuber.Learning tactile skills through curious exploration. Frontiers in Neurorobotics 6:6, 2012, doi: 10.3389/fnbot.2012.00006

24. H. Ngo, M. Luciw, A. Foerster, J. Schmidhuber. Learning Skills from Play: Artificial Curiosity on a Katana Robot Arm. Proc. IJCNN 2012.

23. V. R. Kompella, L. Pape, J. Masci, M. Frank and J. Schmidhuber. AutoIncSFA and Vision-based Developmental Learning for Humanoid Robots. 11th IEEE-RAS International Conference on Humanoid Robots (Humanoids), Bled, Slovenia, 2011.

22. M. Frank, A. Förster, J. Schmidhuber. Reflexive Collision Response with Virtual Skin.International Conference on Agents and Artificial Intelligence ICAART 2012.

21. T. Schaul, J. Bayer, D. Wierstra, S. Yi, M. Felder, F. Sehnke, T. Rückstiess, J. Schmidhuber. PyBrain. Journal of Machine Learning Research (JMLR), 11:743-746, 2010. PDF. (See Pybrain video.)

20. T. Rückstiess, F. Sehnke, T. Schaul, D. Wierstra, S. Yi, J. Schmidhuber. Exploring Parameter Space in Reinforcement Learning.Paladyn Journal of Behavioral Robotics, 2010. PDF.

19. F. Sehnke, C. Osendorfer, T. Rückstiess, A. Graves, J. Peters, J. Schmidhuber. Parameter-exploring policy gradients. Neural Networks 23(2), 2010.PDF.

18. H. Mayer, F. Gomez, D. Wierstra, I. Nagy, A. Knoll, and J. Schmidhuber. A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. Advanced Robotics, 22/13-14, p. 1521-1537, 2008.

17. J. Schmidhuber: Prototype resilient, self-modeling robots. Science 316, no. 5825 p 688, May 2007.

16. F. Gomez, J. Schmidhuber, and R. Miikkulainen (2006). Efficient Non-Linear Control through Neuroevolution. Proceedings of the European Conference on Machine Learning (ECML-06, Berlin).PDF.A new, general method that outperforms many others on difficult control tasks.

15. H. Mayer, F. Gomez, D. Wierstra, I. Nagy, A. Knoll, and J. Schmidhuber (2006). A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks. Proceedings of the International Conference on Intelligent Robotics and Systems (IROS-06, Beijing).PDF.(Best paper nomination finalist.)

14. J. Schmidhuber. Developmental Robotics, Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts.Connection Science, 18(2): 173-187, June 2006.PDF.

13. B. Bakker, V. Zhumatiy, G. Gruener, J. Schmidhuber. Quasi-Online Reinforcement Learning for Robots. Proceedings of the International Conference on Robotics and Automation (ICRA-06), Orlando, Florida, 2006.PDF.A reinforcement learning vision-based robot that learns to build a simple model of the world and itself. To figure out how to achieve rewards in the real world, it performs numerous `mental' experiments using the adaptive world model.

12. V. Zhumatiy, F. Gomez, M. Hutter, and J. Schmidhuber. Metric State Space Reinforcement Learning for a Vision-Capable Mobile Robot. In Proceedings of the International Conference on Intelligent Autonomous Systems, IAS-06, Tokyo, 2006.PDF.

11. F. J. Gomez and J. Schmidhuber. Evolving modular fast-weight networks for control. In W. Duch et al. (Eds.):_Proc. Intl. Conf. on Artificial Neural Networks ICANN'05,_LNCS 3697, pp. 383-389, Springer-Verlag Berlin Heidelberg, 2005.Featuring a 3-wheeled reinforcement learning robot with holonomic drive and distance sensors. Without a teacher it learns to balance a jointed pole indefinitely in a simulated 3D environment confined by walls. PDF.HTML overview.

10. Schmidhuber, J., Zhumatiy, V. and Gagliolo, M. Bias-Optimal Incremental Learning of Control Sequences for Virtual Robots. In Groen, F., Amato, N., Bonarini, A., Yoshida, E., and Kr�se, B., editors: Proceedings of the 8-th conference on Intelligent Autonomous Systems, IAS-8, Amsterdam, The Netherlands, pp. 658-665, 2004. PDF .

9. B. Bakker and J. Schmidhuber.Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization (PDF). In F. Groen, N. Amato, A. Bonarini, E. Yoshida, and B. Kr�se (Eds.),Proceedings of the 8-th Conference on Intelligent Autonomous Systems, IAS-8, Amsterdam, The Netherlands, p. 438-445, 2004.

8. B. Bakker, V. Zhumatiy, G. Gruener, and J. Schmidhuber.A Robot that Reinforcement-Learns to Identify and Memorize Important Previous Observations (PDF). In Proceedings of the 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2003.

7. B. Bakker, F. Linaker, J. Schmidhuber.Reinforcement Learning in Partially Observable Mobile Robot Domains Using Unsupervised Event Extraction.In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), Lausanne, 2002. PDF .

6. B. Bakker.Reinforcement Learning with Long Short-Term Memory.Advances in Neural Information Processing Systems 13 (NIPS'13), 2002. (On J. Schmidhuber's CSEM grant 2002.)

5. M. Wiering, R. Salustowicz, J. Schmidhuber.Model-based reinforcement learning for evolving soccer strategies.In Computational Intelligence in Games, chapter 5. Editors N. Baba and L. Jain. pp. 99-131, 2001. PDF .

4. M. Wiering, R. Salustowicz, J. Schmidhuber.Reinforcement learning soccer teams with incomplete world models.Journal of Autonomous Robots, 7(1):77-88, 1999. PDF .

3. R. Salustowicz and M. Wiering and J. Schmidhuber.Learning team strategies: soccer case studies.Machine Learning 33(2/3), 263-282, 1998 (127 K). PDF .

2. R. Salustowicz and M. Wiering and J. Schmidhuber.Evolving soccer strategies.In N. Kasabov, R. Kozma, K. Ko, R. O'Shea, G. Coghill, and T. Gedeon, editors, Progress in Connectionist-based Information Systems: Proceedings of the Fourth International Conference on Neural Information Processing ICONIP'97, volume 1, pages 502-505, 1997.

1. R. Salustowicz and M. Wiering and J. Schmidhuber.On learning soccer strategies.In W. Gerstner, A. Germond, M. Hasler, J.-D. Nicoud, eds.,Proceedings of the International Conference on Artificial Neural Networks, Lausanne, Switzerland, Springer, 769-774, 1997.