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Cited by 444

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Aggressive driving with model predictive path integral control

Williams, Grady, Drews, Paul, Goldfain, Brian, Rehg, James M., Theodorou, Evangelos A.·2016·2016 IEEE International Conference on Robotics and Automation (ICRA)

Abstract

In this paper we present a model predictive control algorithm designed for optimizing non-linear systems subject to complex cost criteria. The algorithm is based on a stochastic optimal control framework using a fundamental relationship between the information theoretic notions of free energy and relative entropy. The optimal controls in this setting take the form of a path integral, which we approximate using an efficient importance sampling scheme. We experimentally verify the algorithm by implementing it on a Graphics Processing Unit (GPU) and apply it to the problem of controlling a fifth-scale Auto-Rally vehicle in an aggressive driving task.

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Metadata

Authors
Williams, G., Drews, P., Goldfain, B., Rehg, J. M., & Theodorou, E. A.
Venue
2016 IEEE International Conference on Robotics and Automation (ICRA)
Publisher
IEEE
Published
2016-05
Pages
1433-1440
ISSN
1050-4729 (ISSN-L)
OpenAlex
W2410617946
Cited by
444
References
2
FWCI
38.35
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Aggressive driving with model predictive path integral control. https://app.cochat.ai/papers/10.1109/icra.2016.7487277
The paper
Williams, G., Drews, P., Goldfain, B., Rehg, J. M., & Theodorou, E. A. (2016). Aggressive driving with model predictive path integral control. 2016 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra.2016.7487277

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.