Engine Agnostic Graph Environments for Robotics (EAGERx)
A Graph-Based Framework for Sim2real Robot Learning
D.S. van der Heijden (TU Delft - Learning & Autonomous Control)
J.D. Luijkx (TU Delft - Learning & Autonomous Control)
Laura Ferranti (TU Delft - Learning & Autonomous Control)
J. Kober (TU Delft - Learning & Autonomous Control)
R Babuška (TU Delft - Learning & Autonomous Control, Czech Technical University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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