Web-Gewu让网页直接玩机器人强化学习，零安装低延迟。
Web-Gewu: A Browser-Based Interactive Playground for Robot Reinforcement Learning
- 云-边-端协同架构，边缘节点承担仿真与训练任务。
- 浏览器直连，端到端延迟低，支持实时可视化监控数据。
- 适合教育场景，无需本地配置，大规模部署无压力。
随着具身智能的快速发展，机器人教育面临计算门槛高和环境配置复杂双重挑战。现有集中式云仿真方案带来高昂的GPU与带宽成本，难以大规模推广；而纯本地计算又受限于学习者硬件条件。为此，我们提出Web-Gewu，一个基于WebRTC云-边-客户端协同架构的交互式机器人教育平台。系统将全部物理仿真与强化学习（RL）训练任务卸载至边缘节点，云端仅作为轻量级信令中继，实现极低成本的浏览器端点对点（P2P）实时流传输。学习者可在无任何本地安装的情况下，通过网页直接与多形态机器人交互，实现低端到端延迟操作，并实时观察包括强化学习奖励曲线在内的多维监控数据。结合预设的鲁棒命令通信协议，Web-Gewu为具身智能提供高度可扩展、开箱即用、无门槛的教学基础设施，显著降低前沿机器人技术的入门门槛。
原文摘要 · Abstract (English)
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.
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