Learning compound multi-step controllers under unknown dynamics

IEEE International Conference on Intelligent Robots and Systems

Volumn 2015-December, Issue , 2015, Pages 6435-6442

  Han, Weiqiao ^a   Levine, Sergey ^a   Abbeel, Pieter ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Compounds; Heuristic algorithms; Learning (artificial intelligence); Neural networks; Robots; Training; Trajectory

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHAINS; COMPLEX NETWORKS; HEURISTIC ALGORITHMS; INTELLIGENT ROBOTS; NEURAL NETWORKS; PERSONNEL TRAINING; REINFORCEMENT LEARNING; ROBOTS; TRAJECTORIES;

COMPOUNDS; LEARNING (ARTIFICIAL INTELLIGENCE); LOCAL LINEAR MODELS; MULTIPLE CONTROLLERS; NON-STATIONARY LEARNING; NON-STATIONARY PROBLEMS; NONLINEAR NEURAL NETWORKS; ROBOTIC MANIPULATION;

CONTROLLERS;

EID: 84958225623     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2015.7354297     Document Type: Conference Paper

References (23)

1. J. Kober, E. Oztop, and J. Peters, "Reinforcement learning to adjust robot movements to new situations," in Robotics: Science and Systems (RSS), 2010.
2. P. Pastor, H. Hoffmann, T. Asfour, and S. Schaal, "Learning and generalization of motor skills by learning from demonstration," in International Conference on Robotics and Automation (ICRA), 2009.
3. M. Deisenroth, C. Rasmussen, and D. Fox, "Learning to control a low-cost manipulator using data-efficient reinforcement learning," in Robotics: Science and Systems (RSS), 2011.
4. N. Kohl and P. Stone, "Policy gradient reinforcement learning for fast quadrupedal locomotion," in International Conference on Robotics and Automation (IROS), 2004.
5. R. Tedrake, T. Zhang, and H. Seung, "Stochastic policy gradient reinforcement learning on a simple 3d biped," in International Conference on Intelligent Robots and Systems (IROS), 2004.
6. T. Geng, B. Porr, and F. Wörgötter, "Fast biped walking with a reflexive controller and realtime policy searching," in Advances in Neural Information Processing Systems (NIPS), 2006.
7. G. Endo, J. Morimoto, T. Matsubara, J. Nakanishi, and G. Cheng, "Learning CPG-based biped locomotion with a policy gradient method: Application to a humanoid robot," International Journal of Robotic Research, vol. 27, no. 2, pp. 213-228, 2008.
8. S. Levine, N. Wagener, and P. Abbeel, "Learning contact-rich manipulation skills with guided policy search," in International Conference on Robotics and Automation (ICRA), 2015.
9. W. Li and E. Todorov, "Iterative linear quadratic regulator design for nonlinear biological movement systems," in ICINCO (1), 2004, pp. 222-229.
10. J. Peters and S. Schaal, "Reinforcement learning of motor skills with policy gradients," Neural Networks, vol. 21, no. 4, pp. 682-697, 2008.
11. E. Theodorou, J. Buchli, and S. Schaal, "Reinforcement learning of motor skills in high dimensions," in International Conference on Robotics and Automation (ICRA), 2010.
12. S. Levine and P. Abbeel, "Learning neural network policies with guided policy search under unknown dynamics," in Advances in Neural Information Processing Systems (NIPS), 2014.
13. M. Deisenroth, G. Neumann, and J. Peters, "A survey on policy search for robotics," Foundations and Trends in Robotics, vol. 2, no. 1-2, pp. 1-142, 2013.
14. F. Stulp, E. Theodorou, and S. Schaal, "Reinforcement learning with sequences of motion primitives for robust manipulation," Robotics, IEEE Transactions on, vol. 28, no. 6, pp. 1360-1370, Dec 2012.
15. C. Daniel, G. Neumann, O. Kroemer, and J. Peters, "Learning sequential motor tasks," in International Conference on Robotics and Automation (ICRA), 2013.
16. R. Lioutikov, O. Kroemer, J. Peters, and G. Maeda, "Learning manipulation by sequencing motor primitives with a two-armed robot," in International Conference on Intelligent Autonomous Systems (IAS), 2014.
17. S. Manschitz, J. Kober, M. Gienger, and J. Peters, "Learning to sequence movement primitives from demonstrations," in Intelligent Robots and Systems (IROS), 2014.
18. G. Konidaris, S. Kuindersma, R. Grupen, and A. Barto, "Robot learning from demonstration by constructing skill trees," International Journal of Robotics Research, vol. 31, no. 3, pp. 360-375, 2012.
19. G. Konidaris and A. S. Barreto, "Skill discovery in continuous reinforcement learning domains using skill chaining," in Advances in Neural Information Processing Systems (NIPS), 2009.
20. S. Levine, C. Finn, T. Darrell, and P. Abbeel, "End-to-end training of deep visuomotor policies," arXiv preprint arXiv:1504.00702, 2015.
21. S. Boyd and L. Vandenberghe, Convex Optimization. New York, NY, USA: Cambridge University Press, 2004.
22. S. Levine and V. Koltun, "Guided policy search," in International Conference on Machine Learning (ICML), 2013.
23. R. Lioutikov, A. Paraschos, G. Neumann, and J. Peters, "Samplebased information-theoretic stochastic optimal control," in International Conference on Robotics and Automation (ICRA), 2014.