Deep predictive policy training using reinforcement learning

IEEE International Conference on Intelligent Robots and Systems

Volumn 2017-September, Issue , 2017, Pages 2351-2358

  Ghadirzadeh, Ali ^a   Maki, Atsuto ^a   Kragic, Danica ^a   Bjorkman, Marten ^a  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; CHEMICAL ACTIVATION; DEEP LEARNING; DEEP NEURAL NETWORKS; INTELLIGENT ROBOTS; NETWORK ARCHITECTURE; REINFORCEMENT LEARNING; ROBOTS;

ACTION POLICIES; LEARNING FRAMEWORKS; MOTOR ACTIVATION; NETWORK POLICY; OBJECT GRASPING; PREDICTIVE POLICIES; PROPOSED ARCHITECTURES; SIMULATED TRAININGS;

PERSONNEL TRAINING;

EID: 85041944294     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2017.8206046     Document Type: Conference Paper

References (32)

1. D. M. Wolpert, J. Diedrichsen, and J. R. Flanagan, "Principles of sensorimotor learning," Nature Reviews Neuroscience, Vol. 12, no. 12, pp. 739-751, 2011.
2. J. Kober, J. A. Bagnell, and J. Peters, "Reinforcement learning in robotics: A survey," The International Journal of Robotics Research, Vol. 32, no. 11, pp. 1238-1274, 2013.
3. M. P. Deisenroth, G. Neumann, J. Peters et al., "A survey on policy search for robotics," Foundations and Trends in Robotics, Vol. 2, no. 1-2, pp. 1-142, 2013.
4. S. B. Most, B. J. Scholl, E. R. Clifford, and D. J. Simons, "What you see is what you set: sustained inattentional blindness and the capture of awareness." Psychological review, Vol. 112, no. 1, p. 217, 2005.
5. A. Ghadirzadeh, J. Bütepage, A. Maki, D. Kragic, and M. Björkman, "A sensorimotor reinforcement learning framework for physical human-robot interaction," in IROS, 2016.
6. C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, and P. Abbeel, "Deep spatial autoencoders for visuomotor learning," in ICRA, 2016.
7. S. Lange, M. Riedmiller, and A. Voigtlander, "Autonomous reinforcement learning on raw visual input data in a real world application," in IJCNN, 2012.
8. R. Jonschkowski and O. Brock, "State representation learning in robotics: Using prior knowledge about physical interaction," in RSS, 2014.
9. M. Watter, J. Springenberg, J. Boedecker, and M. Riedmiller, "Embed to control: A locally linear latent dynamics model for control from raw images," in NIPS, 2015.
10. N. Wahlström, T. B. Schön, and M. P. Deisenroth, "Learning deep dynamical models from image pixels," IFAC-PapersOnLine, Vol. 48, no. 28, pp. 1059-1064, 2015.
11. H. van Hoof, N. Chen, M. Karl, P. van der Smagt, and J. Peters, "Stable reinforcement learning with autoencoders for tactile and visual data," in IROS, 2016.
12. S. Levine, C. Finn, T. Darrell, and P. Abbeel, "End-to-end training of deep visuomotor policies," Journal of Machine Learning Research, Vol. 17, no. 39, pp. 1-40, 2016.
13. A. Ghadirzadeh, A. Maki, and M. Björkman, "A sensorimotor approach for self-learning of hand-eye coordination," in IROS, 2015.
14. A. Ghadirzadeh, J. Bütepage, D. Kragic, and M. Björkman, "Self-learning and adaptation in a sensorimotor framework," in ICRA, 2016.
15. S. Jodogne and J. H. Piater, "Closed-loop learning of visual control policies," Journal of Artificial Intelligence Research, Vol. 28, pp. 349-391, 2007.
16. Y. Chebotar, M. Kalakrishnan, A. Yahya, A. Li, S. Schaal, and S. Levine, "Path integral guided policy search," arXiv preprint arXiv: 1610.00529.
17. P. Agrawal, A. Nair, P. Abbeel, J. Malik, and S. Levine, "Learning to poke by poking: Experiential learning of intuitive physics," in NIPS, 2016.
18. A. Yahya, A. Li, M. Kalakrishnan, Y. Chebotar, and S. Levine, "Collective robot reinforcement learning with distributed asynchronous guided policy search," arXiv preprint arXiv: 1610.00673.
19. V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al., "Human-level control through deep reinforcement learning," Nature, Vol. 518, no. 7540, pp. 529-533, 2015.
20. T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, "Continuous control with deep reinforcement learning," arXiv preprint arXiv: 1509.02971, 2015.
21. S. Levine and P. Abbeel, "Learning neural network policies with guided policy search under unknown dynamics," in NIPS, 2014.
22. J. T. Betts, Practical methods for optimal control and estimation using nonlinear programming. SIAM, 2010.
23. E. F. Camacho and C. B. Alba, Model predictive control. Springer Science & Business Media, 2013.
24. J. Kocijan, R. Murray-Smith, C. E. Rasmussen, and A. Girard, "Gaussian process model based predictive control," in ACC, 2004.
25. A. J. Ijspeert, J. Nakanishi, and S. Schaal, "Learning attractor landscapes for learning motor primitives," NIPS, 2003.
26. J. Kober and J. R. Peters, "Policy search for motor primitives in robotics," in NIPS, 2009.
27. J. Kober, E. Oztop, and J. Peters, "Reinforcement learning to adjust robot movements to new situations," in RSS, 2010.
28. D. P. Kingma and M. Welling, "Auto-encoding variational Bayes," in ICLR, 2014.
29. C. Doersch, "Tutorial on variational autoencoders," arXiv preprint arXiv: 1606.05908, 2016.
30. Y. Duan, X. Chen, R. Houthooft, J. Schulman, and P. Abbeel, "Benchmarking deep reinforcement learning for continuous control," in ICML, 2016.
31. J. Schulman, S. Levine, P. Abbeel, M. I. Jordan, and P. Moritz, "Trust region policy optimization." in ICML, 2015, pp. 1889-1897.
32. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, "Caffe: Convolutional architecture for fast feature embedding," arXiv preprint arXiv: 1408.5093, 2014.