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.

翻译：仿真到现实（Sim2real），即将学习到的控制策略从仿真环境迁移至真实世界，因其能够高效处理复杂任务而在机器人学领域日益受到关注。仿真到现实方法面临仿真与现实之间不匹配的挑战，这些差异源于物理现象建模的不准确性、异步控制等因素。为此，我们提出了EAGERx，这是一个为真实与仿真机器人学习提供统一软件流程的框架。它支持多种仿真器，并有助于集成状态、动作与时间尺度抽象以促进学习。EAGERx集成的延迟仿真、域随机化特性及提出的同步算法有助于缩小仿真到现实的差距。我们（在机器人学习及其他领域）展示了EAGERx在适应多样化机器人系统与保持仿真行为一致性方面的有效性。EAGERx是开源的，其代码可在https://eagerx.readthedocs.io获取。