The rise of embodied AI has greatly improved the possibility of general mobile agent systems. At present, many evaluation platforms with rich scenes, high visual fidelity and various application scenarios have been developed. In this paper, we present a hybrid framework named NeuronsGym that can be used for policy learning of robot tasks, covering a simulation platform for training policy, and a physical system for studying sim2real problems. Unlike most current single-task, slow-moving robotic platforms, our framework provides agile physical robots with a wider range of speeds, and can be employed to train robotic navigation and confrontation policies. At the same time, in order to evaluate the safety of robot navigation, we propose a safety-weighted path length (SFPL) to improve the safety evaluation in the current mobile robot navigation. Based on this platform, we build a new benchmark for navigation and confrontation tasks under this platform by comparing the current mainstream sim2real methods, and hold the 2022 IEEE Conference on Games (CoG) RoboMaster sim2real challenge. We release the codes of this framework\footnote{\url{https://github.com/DRL-CASIA/NeuronsGym}} and hope that this platform can promote the development of more flexible and agile general mobile agent algorithms.

翻译：具身智能的兴起极大提升了通用移动智能体系统的可能性。目前，已开发出多个具有丰富场景、高视觉保真度及多样化应用场景的评估平台。本文提出一个名为NeuronsGym的混合框架，可用于机器人任务的策略学习，涵盖用于训练策略的仿真平台，以及用于研究Sim2Real问题的物理系统。与当前大多数单任务、慢速移动的机器人平台不同，我们的框架提供具有更宽速度范围的敏捷物理机器人，可用于训练机器人导航与对抗策略。同时，为评估机器人导航的安全性，我们提出一种安全加权路径长度（SFPL），以改进当前移动机器人导航的安全性评估。基于该平台，我们通过对比当前主流Sim2Real方法，构建了导航与对抗任务的新基准，并举办了2022年IEEE游戏会议（CoG）RoboMaster Sim2Real挑战赛。我们已开源该框架的代码\footnote{\url{https://github.com/DRL-CASIA/NeuronsGym}}，期望本平台能推动更灵活、更敏捷的通用移动智能体算法的发展。