With the rapid development of embodied intelligence, robotics education faces a dual challenge: high computational barriers and cumbersome environment configuration. Existing centralized cloud simulation solutions incur substantial GPU and bandwidth costs that preclude large-scale deployment, while pure local computing is severely constrained by learners' hardware limitations. To address these issues, we propose \href{http://47.76.242.88:8080/receiver/index.html}{Web-Gewu}, an interactive robotics education platform built on a WebRTC cloud-edge-client collaborative architecture. The system offloads all physics simulation and reinforcement learning (RL) training to the edge node, while the cloud server acts exclusively as a lightweight signaling relay, enabling extremely low-cost browser-based peer-to-peer (P2P) real-time streaming. Learners can interact with multi-form robots at low end-to-end latency directly in a web browser without any local installation, and simultaneously observe real-time visualization of multi-dimensional monitoring data, including reinforcement learning reward curves. Combined with a predefined robust command communication protocol, Web-Gewu provides a highly scalable, out-of-the-box, and barrier-free teaching infrastructure for embodied intelligence, significantly lowering the barrier to entry for cutting-edge robotics technology.

翻译：随着具身智能技术的快速发展，机器人教育面临计算门槛高与环境配置繁琐的双重挑战。现有集中式云端仿真方案需要承担高昂的GPU与带宽成本，难以实现大规模部署；而纯本地计算方案则受限于学习者硬件性能的严重制约。针对这些问题，我们提出了基于WebRTC云-边-端协同架构的交互式机器人教学平台\href{http://47.76.242.88:8080/receiver/index.html}{Web-Gewu}。该系统将全部物理仿真与强化学习训练任务卸载至边缘节点，云端服务器仅作为轻量级信令转发中继，以此实现极低成本的浏览器端点对点实时流传输。学习者无需任何本地安装即可在网页浏览器中以低端到端延迟与多种形态的机器人进行交互，同时可实时观测包含强化学习奖励曲线在内的多维监控数据可视化。结合预定义的鲁棒指令通信协议，Web-Gewu为具身智能教学提供了高度可扩展、开箱即用、零门槛的教学基础设施，显著降低了前沿机器人技术的入门壁垒。