DISCO: Double Likelihood-free Inference Stochastic Control

• URL: http://arxiv.org/abs/2002.07379v3
• Date: Tue, 26 May 2020 07:08:36 GMT
• Title: DISCO: Double Likelihood-free Inference Stochastic Control
• Authors: Lucas Barcelos, Rafael Oliveira, Rafael Possas, Lionel Ott, and Fabio Ramos
• Abstract summary: We propose to leverage the power of modern simulators and recent techniques in Bayesian statistics for likelihood-free inference. The posterior distribution over simulation parameters is propagated through a potentially non-analytical model of the system. Experiments show that the controller proposed attained superior performance and robustness on classical control and robotics tasks.
• Score: 29.84276469617019
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems. As simulators become more advanced, the analytical tractability of the differential equations and associated numerical solvers incorporated in the simulations diminishes, making them difficult to analyse. A potential solution is the use of probabilistic inference to assess the uncertainty of the simulation parameters given real observations of the system. Unfortunately the likelihood function required for inference is generally expensive to compute or totally intractable. In this paper we propose to leverage the power of modern simulators and recent techniques in Bayesian statistics for likelihood-free inference to design a control framework that is efficient and robust with respect to the uncertainty over simulation parameters. The posterior distribution over simulation parameters is propagated through a potentially non-analytical model of the system with the unscented transform, and a variant of the information theoretical model predictive control. This approach provides a more efficient way to evaluate trajectory roll outs than Monte Carlo sampling, reducing the online computation burden. Experiments show that the controller proposed attained superior performance and robustness on classical control and robotics tasks when compared to models not accounting for the uncertainty over model parameters.

Related papers

• SurroFlow: A Flow-Based Surrogate Model for Parameter Space Exploration and Uncertainty Quantification [17.175947741031674]
  Existing deep learning-based surrogate models facilitate efficient data generation, but fall short in uncertainty quantification, efficient parameter space exploration, and reverse prediction. We introduce SurroFlow, a novel normalizing flow-based surrogate model, to learn the invertible transformation between simulation parameters and simulation outputs. Our framework significantly reduces the computational costs while enhancing the reliability and exploration capabilities of scientific surrogate models.
  arXiv  Detail & Related papers  (2024-07-16T19:08:49Z)
• All-in-one simulation-based inference [19.41881319338419]
  We present a new amortized inference method -- the Simformer -- which overcomes current limitations. The Simformer outperforms current state-of-the-art amortized inference approaches on benchmark tasks. It can be applied to models with function-valued parameters, it can handle inference scenarios with missing or unstructured data, and it can sample arbitrary conditionals of the joint distribution of parameters and data.
  arXiv  Detail & Related papers  (2024-04-15T10:12:33Z)
• Diffusion posterior sampling for simulation-based inference in tall data settings [53.17563688225137]
  Simulation-based inference ( SBI) is capable of approximating the posterior distribution that relates input parameters to a given observation. In this work, we consider a tall data extension in which multiple observations are available to better infer the parameters of the model. We compare our method to recently proposed competing approaches on various numerical experiments and demonstrate its superiority in terms of numerical stability and computational cost.
  arXiv  Detail & Related papers  (2024-04-11T09:23:36Z)
• Multirotor Ensemble Model Predictive Control I: Simulation Experiments [0.0]
  An ensemble-represented Gaussian process performs the backward calculations to determine optimal gains for the initial time. We construct the EMPC for terminal control and regulation problems and apply it to the control of a simulated, identical-twin study.
  arXiv  Detail & Related papers  (2023-05-22T01:32:17Z)
• Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty [68.00748155945047]
  Capturing uncertainty in models of complex dynamical systems is crucial to designing safe controllers. Several approaches use formal abstractions to synthesize policies that satisfy temporal specifications related to safety and reachability. Our contribution is a novel abstraction-based controller method for continuous-state models with noise, uncertain parameters, and external disturbances.
  arXiv  Detail & Related papers  (2022-10-12T07:57:03Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)
• MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories [61.3299263929289]
  Simulation-based inference enables learning the parameters of a model even when its likelihood cannot be computed in practice. One class of methods uses data simulated with different parameters to infer an amortized estimator for the likelihood-to-evidence ratio. We show that this approach can be formulated in terms of mutual information between model parameters and simulated data.
  arXiv  Detail & Related papers  (2021-06-03T12:59:16Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control [0.0]
  We introduce a new algorithm to explicitly consider time-invariant uncertainties in optimal control problems. The main novelty in this paper is to use this combination in an efficient fashion to obtain mean and variance estimates of nonlinear transformations. It is shown how to formulate both chance-constraints and a probabilistic objective for the optimal control problem.
  arXiv  Detail & Related papers  (2021-03-09T14:25:08Z)
• Gaussian Process-based Min-norm Stabilizing Controller for Control-Affine Systems with Uncertain Input Effects and Dynamics [90.81186513537777]
  We propose a novel compound kernel that captures the control-affine nature of the problem. We show that this resulting optimization problem is convex, and we call it Gaussian Process-based Control Lyapunov Function Second-Order Cone Program (GP-CLF-SOCP)
  arXiv  Detail & Related papers  (2020-11-14T01:27:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.