Search Results for author: Rafael Oliveira

Found 13 papers, 4 papers with code

Path Signatures for Diversity in Probabilistic Trajectory Optimisation

no code implementations8 Aug 2023 Lucas Barcelos, Tin Lai, Rafael Oliveira, Paulo Borges, Fabio Ramos

Motion planning can be cast as a trajectory optimisation problem where a cost is minimised as a function of the trajectory being generated.

Motion Planning Variational Inference

Batch Bayesian optimisation via density-ratio estimation with guarantees

1 code implementation22 Sep 2022 Rafael Oliveira, Louis Tiao, Fabio Ramos

Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions.

Bayesian Inference Bayesian Optimisation +2

Adaptive Model Predictive Control by Learning Classifiers

no code implementations13 Mar 2022 Rel Guzman, Rafael Oliveira, Fabio Ramos

Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the presence of environment disturbances.

Bayesian Optimisation Density Ratio Estimation +1

Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty

no code implementations1 Mar 2022 Rel Guzman, Rafael Oliveira, Fabio Ramos

We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performance rewards.

Bayesian Optimisation Model Predictive Control

Near optimal sample complexity for matrix and tensor normal models via geodesic convexity

1 code implementation14 Oct 2021 Cole Franks, Rafael Oliveira, Akshay Ramachandran, Michael Walter

For the matrix normal model, all our bounds are minimax optimal up to logarithmic factors, and for the tensor normal model our bound for the largest factor and overall covariance matrix are minimax optimal up to constant factors provided there are enough samples for any estimator to obtain constant Frobenius error.

Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning

no code implementations NeurIPS 2020 Anthony Tompkins, Rafael Oliveira, Fabio Ramos

The resulting method is based on sparse spectrum Gaussian processes, enabling closed-form solutions, and is extensible to a stacked construction to capture more complex patterns.

Gaussian Processes Inductive Bias

Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control

no code implementations1 Oct 2020 Rel Guzman, Rafael Oliveira, Fabio Ramos

Model predictive control (MPC) has been successful in applications involving the control of complex physical systems.

Bayesian Optimisation Continuous Control +1

DISCO: Double Likelihood-free Inference Stochastic Control

1 code implementation18 Feb 2020 Lucas Barcelos, Rafael Oliveira, Rafael Possas, Lionel Ott, Fabio Ramos

Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems.

Model Predictive Control

Distributional Bayesian optimisation for variational inference on black-box simulators

1 code implementation pproximateinference AABI Symposium 2019 Rafael Oliveira, Lionel Ott, Fabio Ramos

Inverse problems are ubiquitous in natural sciences and refer to the challenging task of inferring complex and potentially multi-modal posterior distributions over hidden parameters given a set of observations.

Bayesian Optimisation Variational Inference

Bayesian optimisation under uncertain inputs

no code implementations21 Feb 2019 Rafael Oliveira, Lionel Ott, Fabio Ramos

In this context, we propose an upper confidence bound (UCB) algorithm for BO problems where both the outcome of a query and the true query location are uncertain.

Bayesian Optimisation

Learning to Race through Coordinate Descent Bayesian Optimisation

no code implementations17 Feb 2018 Rafael Oliveira, Fernando H. M. Rocha, Lionel Ott, Vitor Guizilini, Fabio Ramos, Valdir Grassi Jr

On the other hand, the cost to evaluate the policy's performance might also be high, being desirable that a solution can be found with as few interactions as possible with the real system.

Bayesian Optimisation Car Racing +1

Bayesian Optimisation for Safe Navigation under Localisation Uncertainty

no code implementations7 Sep 2017 Rafael Oliveira, Lionel Ott, Vitor Guizilini, Fabio Ramos

In outdoor environments, mobile robots are required to navigate through terrain with varying characteristics, some of which might significantly affect the integrity of the platform.

Bayesian Optimisation Navigate

Cannot find the paper you are looking for? You can Submit a new open access paper.