EAGERx: Graph-Based Framework for Sim2real Robot Learning
This addresses the problem of transferring learned policies from simulation to reality for roboticists, though it appears incremental as a software framework building on existing sim2real concepts.
The paper tackles the sim2real gap in robotics by introducing EAGERx, a graph-based framework that integrates simulation and real-world learning, demonstrating efficacy in accommodating diverse robotic systems and maintaining consistent behavior.
Sim2real, that is, the transfer of learned control policies from simulation to real world, is an area of growing interest in robotics due to its potential to efficiently handle complex tasks. The sim2real approach faces challenges due to 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 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.