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On Hyper-parameter Tuning for Stochastic Optimization Algorithms
This paper proposes the first-ever algorithmic framework for tuning hype...
03/04/2020 ∙ by Haotian Zhang, et al. ∙
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Representation-Free Model Predictive Control for Dynamic Motions in Quadrupeds
This paper presents a novel Representation-Free Model Predictive Control...
12/18/2020 ∙ by Yanran Ding, et al. ∙
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Stein Variational Model Predictive Control
Decision making under uncertainty is critical to real-world, autonomous ...
11/15/2020 ∙ by Alexander Lambert, et al. ∙
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Neural Network Based Explicit MPC for Chemical Reactor Control
In this paper, we show the implementation of deep neural networks applie...
12/10/2019 ∙ by Karol Kiš, et al. ∙
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Nullspace Structure in Model Predictive Control
Robotic tasks can be accomplished by exploiting different forms of redun...
05/23/2019 ∙ by Hakan Girgin, et al. ∙
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Learn Fast, Forget Slow: Safe Predictive Learning Control for Systems with Unknown, Changing Dynamics Performing Repetitive Tasks
We present a control method for improved repetitive path following for a...
10/15/2018 ∙ by Christopher D. McKinnon, et al. ∙
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Particle MPC for Uncertain and Learning-Based Control
As robotic systems move from highly structured environments to open worl...
04/06/2021 ∙ by Robert Dyro, et al. ∙
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Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
10/01/2020 ∙ by Rel Guzman, et al. ∙
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Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided by an approximate model of the system's dynamics to simulate the effect of control actions. MPC methods also present a few hyper-parameters which may require a relatively expensive tuning process by demanding interactions with the physical system. Therefore, we investigate fine-tuning MPC methods in the context of stochastic MPC, which presents extra challenges due to the randomness of the controller's actions. In these scenarios, performance outcomes present noise, which is not homogeneous across the domain of possible hyper-parameter settings, but which varies in an input-dependent way. To address these issues, we propose a Bayesian optimisation framework that accounts for heteroscedastic noise to tune hyper-parameters in control problems. Empirical results on benchmark continuous control tasks and a physical robot support the proposed framework's suitability relative to baselines, which do not take heteroscedasticity into account.
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