Interactive learning of physical object properties through robot manipulation and database of object measurements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F24%3A00376307" target="_blank" >RIV/68407700:21730/24:00376307 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/24:00376307

Výsledek na webu

<a href="https://doi.org/10.1109/IROS58592.2024.10802249" target="_blank" >https://doi.org/10.1109/IROS58592.2024.10802249</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IROS58592.2024.10802249" target="_blank" >10.1109/IROS58592.2024.10802249</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Interactive learning of physical object properties through robot manipulation and database of object measurements

Popis výsledku v původním jazyce

This work presents a framework for automatically extracting physical object properties, such as material compo sition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework in volves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with mea surement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object properties through Bayesian inference. Experimental evaluation demonstrates effective action selection compared to a baseline and correct termination of the experiments if there is nothing more to be learned. The algorithm proved to behave intelligently when presented with trick objects with material properties in conflict with their appearance. The robot pipeline integrates with a logging module and an online database of objects, con taining over 24,000 measurements of 63 objects with different grippers. All code and data are publicly available, facilitating automatic digitization of objects and their physical properties through exploratory manipulations.

Název v anglickém jazyce

Interactive learning of physical object properties through robot manipulation and database of object measurements

Popis výsledku anglicky

This work presents a framework for automatically extracting physical object properties, such as material compo sition, mass, volume, and stiffness, through robot manipulation and a database of object measurements. The framework in volves exploratory action selection to maximize learning about objects on a table. A Bayesian network models conditional dependencies between object properties, incorporating prior probability distributions and uncertainty associated with mea surement actions. The algorithm selects optimal exploratory actions based on expected information gain and updates object properties through Bayesian inference. Experimental evaluation demonstrates effective action selection compared to a baseline and correct termination of the experiments if there is nothing more to be learned. The algorithm proved to behave intelligently when presented with trick objects with material properties in conflict with their appearance. The robot pipeline integrates with a logging module and an online database of objects, con taining over 24,000 measurements of 63 objects with different grippers. All code and data are publicly available, facilitating automatic digitization of objects and their physical properties through exploratory manipulations.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

ISBN

979-8-3503-7770-5

ISSN

2153-0858

e-ISSN

2153-0866

Počet stran výsledku

8

Strana od-do

7596-7603

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Abu Dhabi

Datum konání akce

14. 10. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001433985300013