RINGS: Collaborative Inference and Learning between Edge Swarms and the Cloud

联网机器人的车队正在我们的道路上，工厂和医院中部署，用于自动驾驶，制造和护士协助等任务。这些机器人正在努力处理越来越多的丰富传感数据，并部署计算和功耗的机器学习（ML）模型。然而，机器人有机会通过下一代（NextG）无线网络查询远程计算资源来增强其智能。然而，研究人员缺乏算法来平衡网络计算的准确性优势与延迟，功率，拥塞和远程计算服务器上的负载的系统成本。因此，该项目正在基于决策理论（即，数学成本效益分析），以决定如何平衡机器人和远程计算，同时只传送任务相关的隐私保护数据。由此产生的通信高效算法旨在通过最小化拥塞来提高NextG网络的弹性。该项目的推广工作旨在使K-12学生能够在远程机器人上制作原型。该项目开发算法，以实现机器人群和云之间的联合推理，学习和控制，同时弹性地适应网络连接和计算可用性的变化。今天的机器人控制算法在很大程度上由机载传感器和本地物理状态提供信息，但实际上忽略了网络的时变状态。因此，他们经常对何时查询云做出次优决策，通常导致过度拥塞。因此，该项目开发了决策理论算法，可以灵活地权衡云计算的准确性优势与系统成本。首先，该项目开发了协作推理算法，该算法使用马尔可夫决策过程来决定是否以及在何处卸载计算。然后，它开发了统计数据采样算法，估计上传新训练数据的边际收益与标签和训练成本。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响力审查标准进行评估，被认为值得支持。

Fleets of networked robots are being deployed on our roads, in factories, and in hospitals for tasks like self-driving, manufacturing, and nurse assistance. These robots are struggling to process growing volumes of rich sensory data and deploy compute-and-power hungry machine learning (ML) models. However, robots have an opportunity to augment their intelligence by querying remote compute resources over next-generation (NextG) wireless networks. However, researchers lack algorithms to balance the accuracy benefits of networked computation with systems costs of delay, power, congestion, and load on remote compute servers. As such, this project is innovating algorithms based on decision theory (i.e., a mathematical cost-benefit analysis) to decide how to balance on-robot and remote computation while only communicating task-relevant, privacy-preserving data. The resulting communication-efficient algorithms aim to improve the resiliency of NextG networks by minimizing congestion. The project’s outreach efforts aim to enable K-12 students to prototype on remote robots. This project develops algorithms to enable joint inference, learning, and control between robotic swarms and the cloud while resiliently adapting to variations in network connectivity and compute availability. Today's robotic control algorithms are largely informed by onboard sensors and a local physical state, but effectively ignore the time-variant state of a network. As such, they often make sub-optimal decisions on when to query the cloud, often leading to excessive congestion. Accordingly, this project develops decision-theoretic algorithms that flexibly trade-off the accuracy benefits of the cloud with systems costs. First, the project develops collaborative inference algorithms that decide whether, and where, to offload computation using a Markov Decision Process. Then, it develops statistical data sampling algorithms that estimate the marginal gain of uploading new training data with labeling and training costs. The final thrust learns compressed representations of video and LiDAR that optimize for ML inference accuracy, as opposed to conventional human perception metrics.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.