research
∙
07/11/2023
Forward Dynamics Estimation from Data-Driven Inverse Dynamics Learning
In this paper, we propose to estimate the forward dynamics equations of ...
share
research
∙
01/30/2023
Learning Control from Raw Position Measurements
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named V...
research
∙
04/26/2021
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
In this paper, we consider the use of black-box Gaussian process (GP) mo...
research
∙
01/28/2021
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
In this paper, we present a Model-Based Reinforcement Learning algorithm...
research
∙
01/21/2021
Model-based Policy Search for Partially Measurable Systems
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) al...
research
∙
02/25/2020
Model-Based Reinforcement Learning for Physical Systems Without Velocity and Acceleration Measurements
In this paper, we propose a derivative-free model learning framework for...
research
∙
05/20/2019
A novel Multiplicative Polynomial Kernel for Volterra series identification
Volterra series are especially useful for nonlinear system identificatio...
research
∙
04/30/2019
A novel geometrically inspired polynomial kernel for robot inverse dynamics
In this paper we introduce a novel data driven inverse dynamics estimato...
research
∙
03/11/2019
Robot kinematic structure classification from time series of visual data
In this paper we present a novel algorithm to solve the robot kinematic ...
research
∙
09/13/2018
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 learn...
research
∙
09/13/2018
