Engine Agnostic Graph Environments for Robotics (EAGERx): A Graph-Based Framework for Sim2real Robot Learning

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00380516" target="_blank" >RIV/68407700:21730/25:00380516 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/MRA.2024.3433172" target="_blank" >https://doi.org/10.1109/MRA.2024.3433172</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MRA.2024.3433172" target="_blank" >10.1109/MRA.2024.3433172</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Engine Agnostic Graph Environments for Robotics (EAGERx): A Graph-Based Framework for Sim2real Robot Learning

Popis výsledku v původním jazyce

Sim2real, that is, the transfer of learned control policies from simulation to the real world, is an area of growing interest in robotics because of its potential to efficiently handle complex tasks. The sim2real approach faces challenges because of mismatches between simulation and reality. These discrepancies arise from inaccuracies in modeling physical phenomena and asynchronous control, among other factors. To this end, we introduce Engine Agnostic Graph Environments for Robotics (EAGERx), a framework with a unified software pipeline for both real and simulated robot learning. It can support various simulators and aids in integrating state, action, and time-scale abstractions to facilitate learning. EAGERx’s integrated delay simulation, domain randomization features, and proposed synchronization algorithm contribute to narrowing the sim2real gap. We demonstrate (in the context of robot learning and beyond) the efficacy of EAGERx in accommodating diverse robotic systems and maintaining consistent simulation behavior. EAGERx is open source, and its code is available at https://eagerx.readthedocs.io.

Název v anglickém jazyce

Engine Agnostic Graph Environments for Robotics (EAGERx): A Graph-Based Framework for Sim2real Robot Learning

Popis výsledku anglicky

Sim2real, that is, the transfer of learned control policies from simulation to the real world, is an area of growing interest in robotics because of its potential to efficiently handle complex tasks. The sim2real approach faces challenges because of mismatches between simulation and reality. These discrepancies arise from inaccuracies in modeling physical phenomena and asynchronous control, among other factors. To this end, we introduce Engine Agnostic Graph Environments for Robotics (EAGERx), a framework with a unified software pipeline for both real and simulated robot learning. It can support various simulators and aids in integrating state, action, and time-scale abstractions to facilitate learning. EAGERx’s integrated delay simulation, domain randomization features, and proposed synchronization algorithm contribute to narrowing the sim2real gap. We demonstrate (in the context of robot learning and beyond) the efficacy of EAGERx in accommodating diverse robotic systems and maintaining consistent simulation behavior. EAGERx is open source, and its code is available at https://eagerx.readthedocs.io.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE ROBOTICS & AUTOMATION MAGAZINE

ISSN

1070-9932

e-ISSN

1558-223X

Svazek periodika

32

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

99-112

Kód UT WoS článku

001290220600001

EID výsledku v databázi Scopus

2-s2.0-85206555718