Today’s robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and object detection. However, resource-constrained robots, like low-power drones, often have insufficient on-board compute resources or power reserves to scalably run the most accurate, state-of-the art neural network compute models. Cloud robotics allows mobile robots the benefit of offloading compute to centralized servers if they are uncertain locally or want to run more accurate, compute-intensive models. However, cloud robotics comes with a key, often understated cost: communicating with the cloud over congested wireless networks may result in latency or loss of data. In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications, which we measure experimentally. In this paper, we formulate a novel Robot Offloading Problem—how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication? We formulate offloading as a sequential decision making problem for robots, and propose a solution using deep reinforcement learning. In both simulations and hardware experiments using state-of-the art vision DNNs, our offloading strategy improves vision task performance by between 1.3 and 2.3\(\times \) of benchmark offloading strategies, allowing robots the potential to significantly transcend their on-board sensing accuracy but with limited cost of cloud communication.

当今的机器人系统越来越多地转向计算成本高昂的模型，例如深度神经网络 (DNN)，以执行定位、感知、规划和对象检测等任务。然而，资源受限的机器人，如低功耗无人机，通常没有足够的机载计算资源或电力储备来可扩展地运行最准确、最先进的神经网络计算模型。云机器人如果移动机器人在本地不确定或想要运行更准确的计算密集型模型，则允许移动机器人将计算卸载到集中式服务器。然而，云机器人有一个关键的、通常被低估的成本：通过拥挤的无线网络与云通信可能会导致数据延迟或丢失。事实上，通过拥挤的网络从多个机器人发送高数据速率视频或 LIDAR 可能导致实时应用程序的延迟，我们通过实验测量。在本文中，我们提出了一个新的机器人卸载问题——机器人应该如何以及何时卸载传感任务，尤其是在不确定的情况下，以提高准确性，同时最大限度地降低云通信成本？我们将卸载制定为机器人的顺序决策问题，并提出了使用深度强化学习的解决方案。在使用最先进视觉 DNN 的模拟和硬件实验中，我们的卸载策略将视觉任务性能提高了基准卸载策略的1.3 到 2.3 \(\times \)，使机器人有潜力显着超越其机载传感精度，但云通信成本有限。