Sequential Gaussian Processes for Online Learning of Nonstationary Functions

Many machine learning problems can be framed in the context of estimating functions, and often these are time-dependent functions that are estimated in real-time as observations arrive. Gaussian processes (GPs) are an attractive choice for modeling real-valued nonlinear functions due to their flexibility and uncertainty quantification. However, the typical GP regression model suffers from several drawbacks: i) Conventional GP inference scales $O(N^{3})$ with respect to the number of observations; ii) updating a GP model sequentially is not trivial; and iii) covariance kernels often enforce stationarity constraints on the function, while GPs with non-stationary covariance kernels are often intractable to use in practice. To overcome these issues, we propose an online sequential Monte Carlo algorithm to fit mixtures of GPs that capture non-stationary behavior while allowing for fast, distributed inference. By formulating hyperparameter optimization as a multi-armed bandit problem, we accelerate mixing for real time inference. Our approach empirically improves performance over state-of-the-art methods for online GP estimation in the context of prediction for simulated non-stationary data and hospital time series data

Integrated process of images and acceleration measurements for damage detection

The use of mobile robots and UAV to catch unthinkable images together with on-site global automated acceleration measurements easy achievable by wireless sensors, able of remote data transfer, have strongly enhanced the capability of defect and damage evaluation in bridges. A sequential procedure is, here, proposed for damage monitoring and bridge condition assessment based on both: digital image processing for survey and defect evaluation and structural identification based on acceleration measurements. A steel bridge has been simultaneously inspected by UAV to acquire images using visible light, or infrared radiation, and monitored through a wireless sensor network (WSN) measuring structural vibrations. First, image processing has been used to construct a geometrical model and to quantify corrosion extension. Then, the consistent structural model has been updated based on the modal quantities identified using the acceleration measurements acquired by the deployed WSN. © 2017 The Authors. Published by Elsevier Ltd

Conjunctive Visual and Auditory Development via Real-Time Dialogue

Human developmental learning is capable of dealing with the dynamic visual world, speech-based dialogue, and their complex real-time association. However, the architecture that realizes this for robotic cognitive development has not been reported in the past. This paper takes up this challenge. The proposed architecture does not require a strict coupling between visual and auditory stimuli. Two major operations contribute to the “abstraction” process: multiscale temporal priming and high-dimensional numeric abstraction through internal responses with reduced variance. As a basic principle of developmental learning, the programmer does not know the nature of the world events at the time of programming and, thus, hand-designed task-specific representation is not possible. We successfully tested the architecture on the SAIL robot under an unprecedented challenging multimodal interaction mode: use real-time speech dialogue as a teaching source for simultaneous and incremental visual learning and language acquisition, while the robot is viewing a dynamic world that contains a rotating object to which the dialogue is referring