Engine Agnostic Graph Environments for Robotics (EAGERx)

A Graph-Based Framework for Sim2real Robot Learning

Journal Article (2025)
Author(s)

Bas van der Heijden (TU Delft - Learning & Autonomous Control)

J.D. Luijkx (TU Delft - Learning & Autonomous Control)

L Ferranti (TU Delft - Learning & Autonomous Control)

J. Kober (TU Delft - Learning & Autonomous Control)

Robert Babuška (TU Delft - Learning & Autonomous Control, Czech Technical University)

Research Group
Learning & Autonomous Control
DOI related publication
https://doi.org/10.1109/MRA.2024.3433172
More Info
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Publication Year
2025
Language
English
Research Group
Learning & Autonomous Control
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/publishing/publisher-deals Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.@en
Issue number
2
Volume number
32
Pages (from-to)
99-112
Reuse Rights

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Abstract

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

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