让机器人自适应通信，减少数据上传量
Learning to Optimize Edge Robotics: A Fast Integrated Perception-Motion-Communication Approach
- 融合感知、运动与通信，动态调整压缩比和传输频率
- 计算复杂度降低10倍以上，支持实时运行
- 适合需要低延迟通信的边缘机器人系统
边缘机器人需频繁传输大体积多模态数据。现有方法忽视机器人功能与通信条件间的相互依赖，导致通信开销过高。本文提出感知-运动-通信一体化（IPMC）框架，使机器人基于感知与运动动态知识，动态调整通信策略（如压缩比、传输频率、发射功率），从而减少传感器数据上传需求。进一步采用学习优化（LTO）范式，设计并实现一种模仿学习神经网络，相比当前最优求解器，计算复杂度降低逾10倍。实验验证了IPMC的优越性及LTO的实时执行能力。
原文摘要 · Abstract (English)
Edge robotics involves frequent exchanges of large-volume multi-modal data. Existing methods ignore the interdependency between robotic functionalities and communication conditions, leading to excessive communication overhead. This paper revolutionizes edge robotics systems through integrated perception, motion, and communication (IPMC). As such, robots can dynamically adapt their communication strategies (i.e., compression ratio, transmission frequency, transmit power) by leveraging the knowledge of robotic perception and motion dynamics, thus reducing the need for excessive sensor data uploads. Furthermore, by leveraging the learning to optimize (LTO) paradigm, an imitation learning neural network is designed and implemented, which reduces the computational complexity by over 10x compared to state-of-the art optimization solvers. Experiments demonstrate the superiority of the proposed IPMC and the real-time execution capability of LTO.
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