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Learning the dynamics of a physical system wherein an autonomous agent operates is an important task.
Very basic lie theory
Howe, R · 1983
Earlier work this paper cites.
Robot analysis: the mechanics of serial and parallel manipulators
Tsai, L.-W · 1999
Earlier work this paper cites.
Scalable techniques from nonparametric statistics for real time robot learning
Schaal, S., Atkeson, C. G., and Vijayakumar, S · 2002
Earlier work this paper cites.
Lie Groups, Lie Algebras, and Representations: An Elementary Introduction
Hall, B. and Hall, B · 2003
Earlier work this paper cites.
Gaussian process model based predictive control
Kocijan, J., Murray-Smith, R., Rasmussen, C. E., and Girard, A · 2004
Earlier work this paper cites.
Lie groups and lie algebras in robotics
Selig, J. M · 2004
Earlier work this paper cites.
A generalized iterative lqg method for locally-optimal feedback control of constrained nonlinear stochastic systems
Todorov, E. and Li, W · 2005
Earlier work this paper cites.
Using model knowledge for learning inverse dynamics
Nguyen-Tuong, D. and Peters, J · 2010
Earlier work this paper cites.
Model learning for robot control: a survey
Nguyen-Tuong, D. and Peters, J · 2011
Earlier work this paper cites.
Information theory of decisions and actions
Tishby, N. and Polani, D · 2011
Earlier work this paper cites.
Data-driven dynamic emulation modelling for the optimal management of environmental systems
Castelletti, A., Galelli, S., Restelli, M., and Soncini-Sessa, R · 2012
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 2012
Earlier work this paper cites.
Control theory from the geometric viewpoint , volume 87
Agrachev, A. A. and Sachkov, Y · 2013
Earlier work this paper cites.
Rigid body dynamics algorithms
Featherstone, R · 2014
Earlier work this paper cites.
Continuous-discrete extended kalman filter on matrix lie groups using concentrated gaussian distributions
Bourmaud, G., Mégret, R., Arnaudon, M., and Giremus, A · 2015
Earlier work this paper cites.
Recurrent network models for human dynamics
Fragkiadaki, K., Levine, S., Felsen, P., and Malik, J · 2015
Earlier work this paper cites.
Embed to control: A locally linear latent dynamics model for control from raw images
Watter, M., Springenberg, J. T., Boedecker, J., and Riedmiller, M · 2015
Earlier work this paper cites.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Cited alongside, same era.
Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
Cited alongside, same era.
Rolling rotations for recognizing human actions from 3d skeletal data
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Cited alongside, same era.
Sample-based approach can outperform the classical dynamical analysis-experimental confirmation of the basin stability method
Brzeski, P., Wojewoda, J., Kapitaniak, T., Kurths, J., and Perlikowski, P · 2017
Cited alongside, same era.
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Byravan, A. and Fox, D · 2017
Cited alongside, same era.
Deep learning on lie groups for skeleton-based action recognition
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Greydanus, S., Dzamba, M., and Yosinski, J · 2019
Later among the works it cites.
When to trust your model: Model-based policy optimization
Janner, M., Fu, J., Zhang, M., and Levine, S · 2019
Later among the works it cites.
Deep lagrangian networks: Using physics as model prior for deep learning
Lutter, M., Ritter, C., and Peters, J · 2019
Later among the works it cites.
Babaeizadeh, M., Saffar, M., Hafner, D., Kannan, H., Finn, C., Levine, S., and Erhan, D · 2020
Later among the works it cites.
Neural dynamic policies for end-to-end sensorimotor learning
Bahl, S., Mukadam, M., Gupta, A., and Pathak, D · 2020
Later among the works it cites.
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Chen, R. T., Rubanova, Y., Bettencourt, J., and Duvenaud, D · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
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Cited alongside, same era.
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Hafner, D., Lillicrap, T., Norouzi, M., and Ba, J · 2020
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Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data
Luo, X. and Kareem, A · 2020
Later among the works it cites.
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Miles, C., Sam, G., Stephan, H., Peter, B., David, S., and Shirley, H · 2020
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Model-based reinforcement learning: A survey
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Deep dynamics models for learning dexterous manipulation
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Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes
Nelles, O · 2020
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Learning group structure and disentangled representations of dynamical environments
Quessard, R., Barrett, T. D., and Clements, W. R · 2020
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A game theoretic framework for model based reinforcement learning
Rajeswaran, A., Mordatch, I., and Kumar, V · 2020
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Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al · 2020
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Shi, Y., Huang, J., Zhang, H., Xu, X., Rusinkiewicz, S., and Xu, K · 2020
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