[39] Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J Andrew Bagnell, Pieter Abbeel, Jan Peters, et al. An algorithmic perspective on imitation learning. Foundations and Trends in Robotics, 7(1-2):1-179, 2018.
[40] Matthew E Taylor and Peter Stone. Transfer learning for reinforcement learning domains: A survey. Journal of Machine Learning Research, 10(7), 2009.
[41] Karl Johan Åström. Optimal control of Markov processes with incomplete state information I. Journal of Mathematical Analysis and Applications, 10:174-205, 1965.
[42] Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra. Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2):99-134, 1998.
[43] Weiwei Li and Emanuel Todorov. Iterative linearization methods for approximately optimal control and estimation of non-linear stochastic system. International Journal of Control, 80(9):1439-1453, 2007.
[44] Hilbert J Kappen, Vicenç Gómez, and Manfred Opper. Optimal control as a graphical model inference problem. Machine learning, 87(2):159-182, 2012.
[45] Marc Toussaint. Robot trajectory optimization using approximate inference. In International Conference on Machine Learning, pages 1049-1056, 2009.
[46] Sergey Levine. Reinforcement learning and control as probabilistic inference: Tutorial and review. arXiv preprint arXiv:1805.00909, 2018.
[47] Adam Gleave and Sam Toyer. A primer on maximum causal entropy inverse reinforcement learning. arXiv preprint arXiv:2203.11409, 2022.
[48] Sergey Levine and Vladlen Koltun. Guided policy search. In International Conference on Machine Learning, pages 1-9, 2013.
[49] R. E. Kalman. A New Approach to Linear Filtering and Prediction Problems. Journal of Basic Engineering, 82(1):35-45, 1960.
[50] Jur Van Den Berg, Pieter Abbeel, and Ken Goldberg. LQG-MP: Optimized path planning for robots with motion uncertainty and imperfect state information. The International Journal of Robotics Research, 30 (7):895-913, 2011.
[51] Ciyou Zhu, Richard H Byrd, Peihuang Lu, and Jorge Nocedal. Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization. ACM Transactions on Mathematical Software (TOMS), 23(4):550-560, 1997.
[52] Arne J Nagengast, Daniel A Braun, and Daniel M Wolpert. Optimal control predicts human performance on objects with internal degrees of freedom. PLoS Computational Biology, 5(6):e1000419, 2009.
[53] David C Knill, Amulya Bondada, and Manu Chhabra. Flexible, task-dependent use of sensory feedback to control hand movements. Journal of Neuroscience, 31(4):1219-1237, 2011.
[54] Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba. Openai gym. arXiv preprint arXiv:1606.01540, 2016.
[55] Robert Platt, Russell Louis Tedrake, Leslie P. Kaelbling, and Tomas Lozano-Perez. Belief space planning assuming maximum likelihood observations. In Proceedings of Robotics: Science and Systems, 2010. doi: 10.15607/RSS.2010.VI.037.
[56] Jur Van Den Berg, Sachin Patil, and Ron Alterovitz. Motion planning under uncertainty using iterative local optimization in belief space. The International Journal of Robotics Research, 31(11):1263-1278, 2012.
[57] Arnaud Doucet, Nando De Freitas, and Neil James Gordon. Sequential Monte Carlo methods in practice. Springer, 2001.
[58] Luigi Acerbi, Wei Ji Ma, and Sethu Vijayakumar. A framework for testing identifiability of bayesian models of perception. In Advances in Neural Information Processing Systems, volume 27, 2014.
[59] Danijar Hafner, Timothy Lillicrap, Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprint arXiv:1912.01603, 2019.