Model predictive impedance control with Gaussian processes for human and environment interaction

Robotic tasks which involve uncertainty – due to variation in goal, environment configuration, or confidence in task model – may require human input to instruct or adapt the robot. In tasks with physical contact, several existing methods for adapting robot trajectory or impedance according to individual uncertainties have been proposed, e.g.,...

Gaussian process repetitive control: Beyond periodic internal models through kernels

Repetitive control enables the exact compensation of periodic disturbances if the internal model is appropriately selected. The aim of this paper is to develop a novel synthesis technique for repetitive control (RC) based on a new more general internal model. By employing a Gaussian process internal model, asymptotic rejection is obtained for...

Three Symmetries for Data-Driven Pedestrian Inertial Navigation

The last years have seen a growing body of literature on data-driven pedestrian inertial navigation. However, despite this, it is still unclear how to efficiently combine classical models and other a priori information with existing machine learning frameworks. In this paper, we first categorize existing approaches to data-driven pedestrian...

Sparse quantum Gaussian processes to counter the curse of dimensionality

Gaussian processes are well-established Bayesian machine learning algorithms with significant merits, despite a strong limitation: lack of scalability. Clever solutions address this issue by inducing sparsity through low-rank approximations, often based on the Nystrom method. Here, we propose a different method to achieve better scalability...

A numerical Bayesian-calibrated characterization method for multiscale prepreg preforming simulations with tension-shear coupling

Carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain...

Adaptive efficient global optimization of systems with independent components

We present a novel approach for efficient optimization of systems consisting of expensive to simulate components and relatively inexpensive system-level simulations. We consider the types of problem in which the components of the system problem are independent in the sense that they do not exchange coupling variables, however, design...

Trajectory driven multidisciplinary design optimization of a sub-orbital spaceplane using non-stationary Gaussian process

This paper presents the multidisciplinary optimization of an aircraft carried sub-orbital spaceplane. The optimization process focused on three disciplines: the aerodynamics, the structure and the trajectory. The optimization of the spaceplane geometry was coupled with the optimization of its trajectory. The structural weight was estimated using...

Correlation inequalities and applications to vector-valued Gaussian random variables and fractional Brownian motion