  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.
