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

06/27/2019
by   Sebastian Riedel, et al.
1

Physical modeling of robotic system behavior is the foundation for controlling many robotic mechanisms to a satisfactory degree. Mechanisms are also typically designed in a way that good model accuracy can be achieved with relatively simple models and model identification strategies. If the modeling accuracy using physically based models is not enough or too complex, model-free methods based on machine learning techniques can help. Of particular interest to us was therefore the question to what degree semi-parametric modeling techniques, meaning combinations of physical models with machine learning, increase the modeling accuracy of inverse dynamics models which are typically used in robot control. To this end, we evaluated semi-parametric Gaussian process regression and a novel model-based neural network architecture, and compared their modeling accuracy to a series of naive semi-parametric, parametric-only and non-parametric-only regression methods. The comparison has been carried out on three test scenarios, one involving a real test-bed and two involving simulated scenarios, with the most complex scenario targeting the modeling a simulated robot's inverse dynamics model. We found that in all but one case, semi-parametric Gaussian process regression yields the most accurate models, also with little tuning required for the training procedure.

READ FULL TEXT

page 1

page 8

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
06/25/2020

Prediction with Gaussian Process Dynamical Models

The modeling and simulation of dynamical systems is a necessary step for...
research
11/17/2020

Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing

Accurately modeling robot dynamics is crucial to safe and efficient moti...
research
05/16/2023

Conditional variational autoencoder with Gaussian process regression recognition for parametric models

In this article, we present a data-driven method for parametric models w...
research
10/05/2019

Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning

Motivated by the recursive Newton-Euler formulation, we propose a novel ...
research
01/18/2016

Incremental Semiparametric Inverse Dynamics Learning

This paper presents a novel approach for incremental semiparametric inve...
research
04/14/2021

Considerations Across Three Cultures: Parametric Regressions, Interpretable Algorithms, and Complex Algorithms

We consider an extension of Leo Breiman's thesis from "Statistical Model...

Please sign up or login with your details

Forgot password? Click here to reset