Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …
essential if it is desired to simultaneously take advantage of both engineering principles and …
A survey on policy search algorithms for learning robot controllers in a handful of trials
Most policy search (PS) algorithms require thousands of training episodes to find an
effective policy, which is often infeasible with a physical robot. This survey article focuses on …
effective policy, which is often infeasible with a physical robot. This survey article focuses on …
Incremental semiparametric inverse dynamics learning
This paper presents a novel approach for incremental semiparametric inverse dynamics
learning. In particular, we consider the mixture of two approaches: Parametric modeling …
learning. In particular, we consider the mixture of two approaches: Parametric modeling …
Derivative-free online learning of inverse dynamics models
This paper discusses online algorithms for inverse dynamics modeling in robotics. Several
model classes, including rigid body dynamics models, data-driven models and …
model classes, including rigid body dynamics models, data-driven models and …
Online semi-parametric learning for inverse dynamics modeling
This paper presents a semi-parametric algorithm for online learning of a robot inverse
dynamics model. It combines the strength of the parametric and non-parametric modeling …
dynamics model. It combines the strength of the parametric and non-parametric modeling …
A review on the applications of reinforcement learning control for power electronic converters
P Chen, J Zhao, K Liu, J Zhou, K Dong… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
In modern micro-grid systems, the control of power electronic converters faces numerous
challenges, including the uncertainty of parameters of the controlled objects, variations in …
challenges, including the uncertainty of parameters of the controlled objects, variations in …
Automated heart and lung auscultation in robotic physical examinations
This letter presents the first implementation of autonomous robotic auscultation of heart and
lung sounds. To select auscultation locations that generate high-quality sounds, a Bayesian …
lung sounds. To select auscultation locations that generate high-quality sounds, a Bayesian …
An LSTM model with optimal feature selection for predictions of tensile behavior and tensile failure of polymer matrix composites
Mechanical properties such as tensile strength, ductility, and tensile modulus are essential
criteria in polymer matrix composites (PMC) design and are determined through the stress …
criteria in polymer matrix composites (PMC) design and are determined through the stress …
Semiparametrical gaussian processes learning of forward dynamical models for navigating in a circular maze
This paper presents a problem of model learning for the purpose of learning how to navigate
a ball to a goal state in a circular maze environment with two degrees of freedom. The …
a ball to a goal state in a circular maze environment with two degrees of freedom. The …
Data-efficient learning for complex and real-time physical problem solving using augmented simulation
Humans quickly solve tasks in novel systems with complex dynamics, without requiring
much interaction. While deep reinforcement learning algorithms have achieved tremendous …
much interaction. While deep reinforcement learning algorithms have achieved tremendous …