发表机构
Toshiba Europe Ltd.（东芝欧洲有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对物理AI对低延迟高数据量视频流的需求，本文设计并实现了一个端到端目标导向通信测试平台，通过传输语义表示（3D边界框、2D/3D场景图）显著降低任务完成时间并提高成功率。
AI 中文摘要
物理AI依赖于频繁更新、对延迟敏感的视频流来感知、推理并与物理世界交互，这导致了对延迟的严格要求以及比现有5G网络所能支持的更高的数据量。目标导向通信（GoC）通过仅传输与任务相关的语义表示，提供了一种有前景的方法来解决这一挑战。然而，现有的GoC框架主要在仿真中评估，其有效性从未在物理AI应用的实际部署中得到验证。在这项工作中，我们为物理AI开发了一个端到端的GoC测试平台，该平台通过5G OpenAirInterface网络将配备RGB-D相机和5G调制解调器的PiPER机械臂连接到NVIDIA Jetson AGX Orin边缘服务器。我们提出并实现了三种GoC框架，分别传输3D边界框、2D场景图和3D场景图作为三种类型的语义表示。它们共享为闭环物理AI应用设计的通用功能模块，包括语义提取、全栈5G传输、语言模型推理、数字孪生验证和机器人控制。在我们测试平台上的大量实验表明，与定期传输原始图像数据的传统框架相比，我们的GoC框架将任务完成时间减少了高达52.6%，并将任务成功概率提高了高达45%。这些结果验证了我们GoC框架的实际有效性，并为未来6G网络上高效可靠的物理AI应用铺平了道路。项目网站：此https URL。
英文摘要
Physical AI relies on frequently-updated, latency-sensitive video stream to perceive, reason, and interact with the physical world, resulting in strict latency requirements with much higher data volumes that existing 5G networks cannot support. Goal-oriented communication (GoC) offers as a promising approach to solve this challenge by transmitting only task-relevant semantic representations. However, existing GoC frameworks were mainly evaluated in the simulations while their effectiveness has never been validated in a practical deployment of physical AI application. In this work, we develop an end-to-end GoC testbed for Physical AI, which connects a PiPER robot arm equipped with an RGB-D camera and a 5G modem to an NVIDIA Jetson AGX Orin edge server through a 5G OpenAirInterface network. We propose and implement three GoC frameworks that transmit 3D bounding boxes, 2D scene graphs, and 3D scene graphs, as three types of semantic representations, respectively. They share the common functional modules designed for closed-loop Physical AI applications, including semantic extraction, full stack 5G transmission, language model inference, digital twin validation, and robotic control. Extensive experiments on our testbed show that our GoC frameworks reduce the task completion time by up to 52.6% and improve task success probability by up to 45%, compared to the traditional framework that periodically transmits the raw image data. These results validate the practical effectiveness of our GoC framework and pave the way for efficient and reliable Physical AI applications over future 6G networks. Project website: https://sites.google.com/view/goc-physical-ai-testbed.
CommentsSubmitting to IEEE for potential publications
