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Estimation of Gaussian process regression model using probability distance measures
A new class of parameter estimation algorithms is introduced for Gaussian process regression (GPR) models. It is shown that the integration of the GPR model with probability distance measures of (i) the integrated square error and (ii) Kullback–Leibler (K–L) divergence are analytically tractable. An efficient coordinate descent algorithm is proposed to iteratively estimate the kernel width using golden section search which includes a fast gradient descent algorithm as an inner loop to estimate the noise variance. Numerical examples are included to demonstrate the effectiveness of the new identification
approaches
Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics
The most data-efficient algorithms for reinforcement learning in robotics are
model-based policy search algorithms, which alternate between learning a
dynamical model of the robot and optimizing a policy to maximize the expected
return given the model and its uncertainties. Among the few proposed
approaches, the recently introduced Black-DROPS algorithm exploits a black-box
optimization algorithm to achieve both high data-efficiency and good
computation times when several cores are used; nevertheless, like all
model-based policy search approaches, Black-DROPS does not scale to high
dimensional state/action spaces. In this paper, we introduce a new model
learning procedure in Black-DROPS that leverages parameterized black-box priors
to (1) scale up to high-dimensional systems, and (2) be robust to large
inaccuracies of the prior information. We demonstrate the effectiveness of our
approach with the "pendubot" swing-up task in simulation and with a physical
hexapod robot (48D state space, 18D action space) that has to walk forward as
fast as possible. The results show that our new algorithm is more
data-efficient than previous model-based policy search algorithms (with and
without priors) and that it can allow a physical 6-legged robot to learn new
gaits in only 16 to 30 seconds of interaction time.Comment: Accepted at ICRA 2018; 8 pages, 4 figures, 2 algorithms, 1 table;
Video at https://youtu.be/HFkZkhGGzTo ; Spotlight ICRA presentation at
https://youtu.be/_MZYDhfWeL
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
Novel methods for real-time 3D facial recognition
In this paper we discuss our approach to real-time 3D face recognition. We argue the need for real time operation in a realistic scenario and highlight the required pre- and post-processing operations for effective 3D facial recognition. We focus attention to some operations including face and eye detection, and fast post-processing operations such as hole filling, mesh smoothing and noise removal. We consider strategies for hole filling such as bilinear and polynomial interpolation and Laplace and conclude that bilinear interpolation is preferred. Gaussian and moving average smoothing strategies are compared and it is shown that moving average can have the edge over Gaussian smoothing. The regions around the eyes normally carry a considerable amount of noise and strategies for replacing the eyeball with a spherical surface and the use of an elliptical mask in conjunction with hole filling are compared. Results show that the elliptical mask with hole filling works well on face models and it is simpler to implement. Finally performance issues are considered and the system has demonstrated to be able to perform real-time 3D face recognition in just over 1s 200ms per face model for a small database
On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors
In this paper we address both to the problem of identifying the noise Power
Spectral Density of interferometric detectors by parametric techniques and to
the problem of the whitening procedure of the sequence of data. We will
concentrate the study on a Power Spectral Density like the one of the
Italian-French detector VIRGO and we show that with a reasonable finite number
of parameters we succeed in modeling a spectrum like the theoretical one of
VIRGO, reproducing all its features. We propose also the use of adaptive
techniques to identify and to whiten on line the data of interferometric
detectors. We analyze the behavior of the adaptive techniques in the field of
stochastic gradient and in the
Least Squares ones.Comment: 28 pages, 21 figures, uses iopart.cls accepted for pubblication on
Classical and Quantum Gravit
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