Search Results for author:
Found 6 papers, 1 papers with code
FollowMe: a Robust Person Following Framework Based on Re-Identification and Gestures
Human-robot interaction (HRI) has become a crucial enabler in houses and industries for facilitating operational flexibility.
Learning Control from Raw Position Measurements
We propose a Model-Based Reinforcement Learning (MBRL) algorithm named VF-MC-PILCO, specifically designed for application to mechanical systems where velocities cannot be directly measured.
Control of Mechanical Systems via Feedback Linearization Based on Black-Box Gaussian Process Models
We tested the two strategies on a simulated manipulator with seven degrees of freedom, also varying the GP kernel choice.
Controlled Gaussian Process Dynamical Models with Application to Robotic Cloth Manipulation
A CGPDM is constituted by a low-dimensional latent space, with an associated dynamics where external control variables can act and a mapping to the observation space.
Model-Based Policy Search Using Monte Carlo Gradient Estimation with Real Systems Application
The algorithm relies on Gaussian Processes (GPs) to model the system dynamics and on a Monte Carlo approach to estimate the policy gradient.
Model-based Policy Search for Partially Measurable Systems
In this paper, we propose a Model-Based Reinforcement Learning (MBRL) algorithm for Partially Measurable Systems (PMS), i. e., systems where the state can not be directly measured, but must be estimated through proper state observers.
