Web-Gewu: A Browser-Based Interactive Playground for Robot Reinforcement Learning

2026-04-18

AI Summary

This work addresses the dual challenges of high computational barriers and complex environment setup in robot reinforcement learning education, compounded by the trade-off between cloud costs and limited local resources. To overcome these issues, the authors propose a WebRTC-based cloud-edge-end协同 architecture that offloads physics simulation and reinforcement learning training to edge nodes, while the cloud serves only as a lightweight signaling relay. This design enables the first browser-based, installation-free, and deployment-free peer-to-peer educational platform. By integrating a custom robust communication protocol with edge computing, the system supports real-time interaction with diverse robotic morphologies, live training monitoring, and multidimensional data visualization under extremely low bandwidth and GPU overhead. The approach substantially lowers the entry barrier for embodied AI education and facilitates highly scalable, out-of-the-box large-scale deployment.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• robotics education
• computational barriers
• environment configuration
• reinforcement learning
• embodied intelligence

Innovation

• WebRTC
• edge computing
• reinforcement learning
• browser-based robotics
• cloud-edge-client architecture