A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
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DOI：
10.1109/icra.2019.8793690
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发表时间：
2019-03
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通讯作者：
A. Tanwani;Nitesh Mor;J. Kubiatowicz;Joseph E. Gonzalez;Ken Goldberg
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作者：
A. Tanwani;Nitesh Mor;J. Kubiatowicz;Joseph E. Gonzalez;Ken Goldberg
工业、汽车和服务机器人的需求不断增长，在隐私、安全、延迟、带宽和可靠性方面对集中式云机器人模型提出了挑战。在本文中，我们提出了一种用于深度机器人学习的“雾机器人”方法，该方法以联合方式在云和边缘之间分配计算，存储和网络资源。深度模型在云中的非私有（公共）合成图像上进行训练;模型适用于可信网络内边缘环境的私有真实的图像，随后部署为网络中其他机器人的低延迟和安全推理/预测服务。我们将这种方法应用于表面整理，其中移动的机器人通过学习深度对象识别和抓取规划模型来从杂乱的地板中拾取和分类对象。实验表明，与只使用云或边缘资源相比，Fog Robotics可以通过模拟到真实的域自适应来提高性能，同时将推理周期时间减少4\times $，从而在213次尝试中成功地清理了86%的对象。
The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a ‘Fog Robotics’ approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for low-latency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by $4\times$ to successfully declutter 86% of objects over 213 attempts.