PEERNet：面向实时网络化机器人系统的端到端分析工具
PEERNet: An End-to-End Profiling Tool for Real-Time Networked Robotic Systems
AI总结：
针对网络化机器人系统任务卸载依赖精确性能指标但受实时不确定性影响的问题，提出端到端实时分析工具PEERNet，通过自适应组件分析实现异构硬件监控，经实验验证其有效性与适应性，代码开源。
摘要：
网络化机器人系统在自动驾驶车辆、无人机集群和远程手术等应用中平衡计算、功耗和延迟约束。该领域的核心问题是决定何时以通信延迟为代价，将计算密集型任务卸载到云端或远程服务器。任务卸载算法通常依赖于系统特定性能指标的精确知识，如传感器数据速率、网络带宽和机器学习模型延迟。虽然这些指标可在系统设计期间建模，但连接质量、服务器负载和硬件条件的不确定性会引入实时性能变化，从而阻碍整体性能。我们提出PEERNet，一种面向云机器人的端到端实时分析工具。PEERNet通过对传感器、网络、深度学习管道和设备等系统组件进行有针对性且自适应的分析，实现异构硬件上的性能监控。我们通过网络化机器人任务展示PEERNet的能力，如Franka Emika Panda机械臂的基于图像的远程操作，以及使用Nvidia Jetson Orin查询视觉语言模型。PEERNet揭示机器人系统中的非直观行为，如不对称网络传输和双峰语言模型输出。我们的评估强调了网络化机器人基准测试的有效性和重要性，证明了PEERNet的适应性。我们的代码是开源的，可在github.com/UTAustin-SwarmLab/PEERNet获取。
英文摘要：
Networked robotic systems balance compute, power, and latency constraints in applications such as self-driving vehicles, drone swarms, and teleoperated surgery. A core problem in this domain is deciding when to offload a computationally expensive task to the cloud, a remote server, at the cost of communication latency. Task offloading algorithms often rely on precise knowledge of system-specific performance metrics, such as sensor data rates, network bandwidth, and machine learning model latency. While these metrics can be modeled during system design, uncertainties in connection quality, server load, and hardware conditions introduce real-time performance variations, hindering overall performance. We introduce PEERNet, an end-to-end and real-time profiling tool for cloud robotics. PEERNet enables performance monitoring on heterogeneous hardware through targeted yet adaptive profiling of system components such as sensors, networks, deep-learning pipelines, and devices. We showcase PEERNet's capabilities through networked robotics tasks, such as image-based teleoperation of a Franka Emika Panda arm and querying vision language models using an Nvidia Jetson Orin. PEERNet reveals non-intuitive behavior in robotic systems, such as asymmetric network transmission and bimodal language model output. Our evaluation underscores the effectiveness and importance of benchmarking in networked robotics, demonstrating PEERNet's adaptability. Our code is open-source and available at github.com/UTAustin-SwarmLab/PEERNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
