The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems
Abstract
We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been restricted to deterministic unconstrained problems with quadratic costs. The new method constructs an affine feedback control law, obtained by minimizing a novel quadratic approximation to the optimal cost-to-go function. Global convergence is guaranteed through a Levenberg-Marquardt method; convergence in the vicinity of a local minimum is quadratic. Performance is illustrated on a limited-torque inverted pendulum problem, as well as a complex biomechanical control problem involving a stochastic model of the human arm, with 10 state dimensions and 6 muscle actuators. A Matlab implementation of the new algorithm is availabe at www.cogsci.ucsd.edu//spl sim/todorov.
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Citations
Cites
Optimal feedback control as a theory of motor coordination
Todorov, E., & Jordan, M. I. (2002) · Nature Neuroscience · doi:10.1038/nn963 · Cited by 3,081
Self-citationSignal-dependent noise determines motor planning
Harris, C. M., & Wolpert, D. M. (1998) · Nature · doi:10.1038/29528 · Cited by 2,498
Optimality principles in sensorimotor control
Todorov, E. (2004) · Nature Neuroscience · doi:10.1038/nn1309 · Cited by 1,746
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Cited by
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards
Vecerik, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., & Riedmiller, M. (2017) · arXiv · doi:10.48550/arxiv.1707.08817 · Cited by 478
An algorithmic perspective on imitation learning
Osa, T., Pajarinen, J., Neumann, G., Bagnell, J. A., Abbeel, P., & Peters, J. (2018) · Foundations and Trends in Robotics · doi:10.1561/2300000053 · Cited by 405
A policy-blending formalism for shared control
Dragan, A. D., & Srinivasa, S. S. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913490324 · Cited by 387
A Differentiable Physics Engine for Deep Learning in Robotics
Degrave, J., Hermans, M., Dambre, J., & wyffels, F. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00006 · Cited by 182
Metadata
- Authors
- Todorov, E., & Li, W.
- Venue
- Proceedings of the 2005, American Control Conference, 2005.
- Publisher
- IEEE
- Published
- 2005-08-10
- Pages
- 300-306
- ISSN
- 0743-1619 (ISSN-L)
- OpenAlex
- W2167856595
- Cited by
- 611
- References
- 3
- FWCI
- 12.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Todorov, E., & Li, W. (2005). A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems. Proceedings of the 2005, American Control Conference, 2005. https://doi.org/10.1109/acc.2005.1469949
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