A. Paraschos, C. Daniel, J. R. Peters, and G. Neumann. Probabilistic movement primitives. In Advances in Neural Information Processing Systems (NIPS), pages 2616-2624, 2013.
 P. Englert, A. Paraschos, M. P. Deisenroth, and J. Peters. Probabilistic model-based imitation learning. Adaptive Behavior, 21(5):388-403, 2013.
 S. Levine and V. Koltun. Guided policy search. In Proc. Intl Conf. on Machine Learning (ICML), pages 1-9, 2013.
 S. Calinon. A tutorial on task-parameterized movement learning and retrieval. Intelligent Service Robotics, 9(1):1-29, 2016.
 S. Calinon and A. Billard. Statistical learning by imitation of competing constraints in joint space and task space. Advanced Robotics, 23(15):2059-2076, 2009. [OpenAIRE]
 A. Paraschos, R. Lioutikov, J. Peters, and G. Neumann. Probabilistic prioritization of movement primitives. IEEE Robotics and Automation Letters, 2(4):2294-2301, 2017.
 S. Niekum, S. Osentoski, G. Konidaris, S. Chitta, B. Marthi, and A. G. Barto. Learning grounded finite-state representations from unstructured demonstrations. The International Journal of Robotics Research, 34(2):131-157, 2015.
 M. Mu¨hlig, M. Gienger, J. J. Steil, and C. Goerick. Automatic selection of task spaces for imitation learning. In Proc. IEEE/RSJ Intl Conf. on Intelligent Robots and Systems (IROS), pages 4996-5002, 2009. [OpenAIRE]
 G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural computation, 14(8):1771-1800, 2002.
 H. Zen, M. J. Gales, Y. Nankaku, and K. Tokuda. Product of experts for statistical parametric speech synthesis. IEEE Transactions on Audio, Speech, and Language Processing, 20(3): 794-805, 2012.
 B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey. Maximum entropy inverse reinforcement learning. In AAAI, volume 8, pages 1433-1438. Chicago, IL, USA, 2008.
 C. Finn, S. Levine, and P. Abbeel. Guided cost learning: Deep inverse optimal control via policy optimization. In Proc. Intl Conf. on Machine Learning (ICML), pages 49-58, 2016.
 M. Kalakrishnan, P. Pastor, L. Righetti, and S. Schaal. Learning objective functions for manipulation. In Proc. IEEE Intl Conf. on Robotics and Automation (ICRA), pages 1331-1336, 2013.
 I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), pages 2672-2680, 2014.
 C. Finn, P. Christiano, P. Abbeel, and S. Levine. A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models. NeurIPS Workshop on Adversarial Training, 2016.