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Found 13 papers, 4 papers with code
Path Signatures for Diversity in Probabilistic Trajectory Optimisation
Motion planning can be cast as a trajectory optimisation problem where a cost is minimised as a function of the trajectory being generated.
Batch Bayesian optimisation via density-ratio estimation with guarantees
Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions.
Adaptive Model Predictive Control by Learning Classifiers
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 for Robust Model Predictive Control under Model Parameter Uncertainty
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.
Near optimal sample complexity for matrix and tensor normal models via geodesic convexity
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.
Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning
We consider the regret minimization problem in reinforcement learning (RL) in the episodic setting.
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
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.
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems.
DISCO: Double Likelihood-free Inference Stochastic Control
Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems.
Distributional Bayesian optimisation for variational inference on black-box simulators
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 under uncertain inputs
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.
Learning to Race through Coordinate Descent Bayesian Optimisation
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 for Safe Navigation under Localisation Uncertainty
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.
