A Quantifiable Stratification Strategy for Tidy-up in Service Robotics

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00352482" target="_blank" >RIV/68407700:21230/21:00352482 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/ARSO51874.2021.9542842" target="_blank" >https://doi.org/10.1109/ARSO51874.2021.9542842</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ARSO51874.2021.9542842" target="_blank" >10.1109/ARSO51874.2021.9542842</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Quantifiable Stratification Strategy for Tidy-up in Service Robotics

Popis výsledku v původním jazyce

This paper addresses the problem of tidying up a living room in a messy condition with a service robot (i.e. domestic mobile manipulator). One of the key issues in completing such a task is how to continuously select the object to grasp and take it to the delivery area, especially when the robot works in constrained and partially observable environments. In this paper, we propose a quantifiable stratification method that allows the robot to find feasible action plans according to different configurations of objects-deposits, in order to smoothly deliver the objects to the target deposits. Specifically, it leverages a finite-state machine obeying the principle of Occam's razor (called O- FSM), which is designed to integrate arbitrary user-defined action plans typically ranging from simple to complex. Instead of considering a sophisticated model for the ever-changing objects-deposits configuration in the tidy-up task, we empower the robot to make simple yet effective decisions based on its current faced configuration under a generalized framework. Through scenario planning and simulation experiments with the explicitly designed test cases based on the real robot and the real competition scene, the effectiveness of our method is illustrated.

Název v anglickém jazyce

A Quantifiable Stratification Strategy for Tidy-up in Service Robotics

Popis výsledku anglicky

This paper addresses the problem of tidying up a living room in a messy condition with a service robot (i.e. domestic mobile manipulator). One of the key issues in completing such a task is how to continuously select the object to grasp and take it to the delivery area, especially when the robot works in constrained and partially observable environments. In this paper, we propose a quantifiable stratification method that allows the robot to find feasible action plans according to different configurations of objects-deposits, in order to smoothly deliver the objects to the target deposits. Specifically, it leverages a finite-state machine obeying the principle of Occam's razor (called O- FSM), which is designed to integrate arbitrary user-defined action plans typically ranging from simple to complex. Instead of considering a sophisticated model for the ever-changing objects-deposits configuration in the tidy-up task, we empower the robot to make simple yet effective decisions based on its current faced configuration under a generalized framework. Through scenario planning and simulation experiments with the explicitly designed test cases based on the real robot and the real competition scene, the effectiveness of our method is illustrated.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

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

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of IEEE Workshop on Advanced Robotics and its Social Impacts

ISBN

978-1-6654-4953-3

ISSN

2162-7576

e-ISSN

2162-7568

Počet stran výsledku

6

Strana od-do

182-187

Název nakladatele

IEEE Xplore

Místo vydání

—

Místo konání akce

Virtual Conference

Datum konání akce

8. 7. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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