Benchmarking Incremental Regressors in Traversal Cost Assessment

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00336157" target="_blank" >RIV/68407700:21230/19:00336157 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-030-30487-4_52" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-030-30487-4_52</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-30487-4_52" target="_blank" >10.1007/978-3-030-30487-4_52</a>

Result language

angličtina

Original language name

Benchmarking Incremental Regressors in Traversal Cost Assessment

Original language description

Motivated by the deployment of multi-legged walking robots in traversing various terrain types, we benchmark existing online and unsupervised incremental learning approaches in traversal cost prediction. The traversal cost is defined by the proprioceptive signal of the robot traversal stability that is combined with appearance and geometric properties of the traversed terrains to construct the traversal cost model incrementally. In the motivational deployment, such a model is instantaneously utilized to extrapolate the traversal cost for observed areas that have not yet been visited by the robot to avoid difficult terrains in motion planning. The examined approaches are Incremental Gaussian Mixture Network, Growing Neural Gas, Improved Self-Organizing Incremental Neural Network, Locally Weighted Projection Regression, and Bayesian Committee Machine with Gaussian Process Regressors. The performance is examined using a dataset of the various terrains traversed by a real hexapod walking robot. A part of the presented benchmarking is thus a description of the dataset and also a construction of the reference traversal cost model that is used for comparison of the evaluated regressors. The reference is designed as a compound Gaussian process-based model that is learned separately over the individual terrain types. Based on the evaluation results, the best performance among the examined regressors is provided by Incremental Gaussian Mixture Network, Improved Self-Organizing Incremental Neural Network, and Locally Weighted Projection Regression, while the latter two have the lower computational requirements.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA18-18858S" target="_blank" >GA18-18858S: Robotic Lifelong Learning of Multi-legged Robot Locomotion Control in Autonomous Data Collection Missions</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation. ICANN 2019

ISBN

978-3-030-30486-7

ISSN

0302-9743

e-ISSN

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Number of pages

13

Pages from-to

685-697

Publisher name

Springer

Place of publication

Basel

Event location

Munich

Event date

Sep 17, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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