Neural networks have been successfully employed in various domains such as
classification, regression and clustering, etc. Generally, the back propagation (BP) based …

In the last few years, there has been an exponential increase in the usage of the
autonomous vehicles across the globe. It is due to an exponential increase in the popularity …

Model-free deep reinforcement learning algorithms have been shown to be capable of
learning a wide range of robotic skills, but typically require a very large number of samples …

In this article, an admittance-based controller for physical human-robot interaction (pHRI) is
presented to perform the coordinated operation in the constrained task space. An …

Reinforcement learning holds the promise of enabling autonomous robots to learn large
repertoires of behavioral skills with minimal human intervention. However, robotic …

Gaussian process (GP) regression has been widely used in supervised machine learning
due to its flexibility and inherent ability to describe uncertainty in function estimation. In the …

For spline regressions, it is well known that the choice of knots is crucial for the performance
of the estimator. As a general learning framework covering the smoothing splines, learning …

Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …

In the rapidly evolving landscape of autonomous driving, the capability to accurately predict
future events and assess their implications is paramount for both safety and efficiency …

In this brief, the output reachable estimation and safety verification problems for multilayer
perceptron (MLP) neural networks are addressed. First, a conception called maximum …