Incremental Semiparametric Inverse Dynamics Learning

01/18/2016
by   Raffaello Camoriano, et al.
0

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. This yields to an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.

READ FULL TEXT
research
03/17/2016

Online semi-parametric learning for inverse dynamics modeling

This paper presents a semi-parametric algorithm for online learning of a...
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 ...
research
05/27/2022

End-to-End Learning of Hybrid Inverse Dynamics Models for Precise and Compliant Impedance Control

It is well-known that inverse dynamics models can improve tracking perfo...
research
05/17/2019

Modeling of Missing Dynamical Systems: Deriving Parametric Models using a Nonparametric Framework

In this paper, we consider modeling missing dynamics with a non-Markovia...
research
06/27/2019

Comparing Semi-Parametric Model Learning Algorithms for Dynamic Model Estimation in Robotics

Physical modeling of robotic system behavior is the foundation for contr...
research
11/02/2020

Fast Reinforcement Learning with Incremental Gaussian Mixture Models

This work presents a novel algorithm that integrates a data-efficient fu...
research
09/13/2018

Derivative-free online learning of inverse dynamics models

This paper discusses online algorithms for inverse dynamics modelling in...

Please sign up or login with your details

Forgot password? Click here to reset