当机器人交换意义：面向协作机器人的目标导向语义通信演示
When Robots Exchange Meaning: A Demo of Goal-Oriented Semantic Communications for Collaborative Robotics
AI总结：
本文提出一种机器人-边缘端语义通信测试平台，通过VQ-VAE等技术实现视觉压缩与语义建图，为协作机器人及相关6G网络研究提供了实用演示平台。
AI中文摘要：
协作机器人是面向任务的典型6G用例，其通信质量应体现在任务执行、环境感知与闭环操作中，而非仅关注数据包传输。本文演示了一种机器人-边缘端语义通信（SemCom）测试平台，集成机器人端视觉压缩、边缘端语义建图及基于仪表板的任务交互。搭载RGB-D传感与激光雷达的移动机器人运行ROS 2，Jetson Orin边缘节点执行重建、RTAB-Map建图、语义对象处理及基于浏览器的可视化。作为概念验证，机器人端用ONNX Runtime编码器将RGB帧编码为VQ-VAE token，边缘端用PyTorch解码器重建；320×240图像由80×60 token网格表示，载荷为5400字节，较模型输入RGB字节减少42.67倍。重建的视觉流结合深度、位姿与3D建图信息，生成面向下游机器人应用的语义地图。该演示展示了从语义视觉传输到对象级地图交互的完整路径，为未来任务感知型6G网络研究提供了实用平台，相关研究涉及SemCom、具身AI与物理AI赋能的机器人领域，演示视频可访问指定URL。
英文摘要：
Collaborative robotics is a representative task-oriented 6G use-case, where communication quality should be reflected in mission execution, environment understanding, and closed-loop operation rather than packet delivery alone. This demo paper presents a robot-edge semantic communication (SemCom) testbed integrating robot-side visual compression, edge-side semantic mapping, and dashboard-based mission interaction. A mobile robot equipped with RGB-D sensing and LiDAR runs ROS 2, while a Jetson Orin edge node performs reconstruction, RTAB-Map mapping, semantic object handling, and browserbased visualization. As an initial proof of concept, RGB frames are encoded on the robot into VQ-VAE tokens using an ONNX Runtime encoder and reconstructed on the edge using a PyTorch decoder. A 320 X 240 image is represented by an 80 X 60 token grid with a packed payload of 5400 bytes, corresponding to a 42.67X reduction relative to model-input RGB bytes. The reconstructed visual stream is further associated with depth, pose, and 3D mapping information to generate a semantic map for downstream robotic applications. The demo exposes the full path from semantic visual transport to object-level map interaction, and provides a practical platform for future task-aware 6G networking studies at the intersection of SemCom, embodied AI, and physical AI-enabled robotics. A video of the demo is available at https://tinyurl.com/Tos09
