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.

翻译：边缘机器人技术涉及频繁交换大容量的多模态数据。现有方法忽略了机器人功能与通信条件之间的相互依赖性，导致过度的通信开销。本文通过集成感知、运动与通信（IPMC）彻底革新了边缘机器人系统。如此，机器人能够利用对机器人感知与运动动力学的认知，动态调整其通信策略（即压缩比、传输频率、发射功率），从而减少对过量传感器数据上传的需求。此外，通过利用学习优化（LTO）范式，我们设计并实现了一个模仿学习神经网络，与最先进的优化求解器相比，其计算复杂度降低了超过10倍。实验证明了所提出的IPMC方法的优越性以及LTO的实时执行能力。