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

RINGS：边缘群和云之间的协作推理和学习

• 批准号: 2148186
• 负责人: Sandeep Chinchali
• 金额: $ 85万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2148186&HistoricalAwards=false
• 关键词: RINGS; Collaborative; Inference; Learning; between

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.

网络机器人车队正在我们的道路、工厂和医院中部署，以执行自动驾驶、制造和护士协助等任务，这些机器人正在努力处理不断增长的丰富传感数据并部署需要计算和电力的任务。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，研究人员缺乏平衡网络计算的准确性优势与系统延迟成本的算法。 、电力、拥堵、因此，该项目正在基于决策理论（即数学成本效益分析）创新算法，以决定如何平衡机器人和远程计算，同时仅进行与任务相关、保护隐私的通信。由此产生的高效通信算法旨在通过最大限度地减少拥塞来提高 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进行原型开发。机器人群和云之间的学习和控制，同时弹性地适应网络连接和计算可用性的变化。当今的机器人控制算法主要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，他们经常在何时查询云方面做出次优决策，从而常常导致过度拥塞。因此，该项目开发了决策理论算法，可以灵活地权衡云的准确性优势与系统成本。项目开发协作推理算法，决定是否以及在何处使用马尔可夫决策过程卸载计算。然后，它开发统计数据采样算法，通过标签和训练成本来估计上传新训练数据的边际收益。与传统的人类感知指标相比，视频和激光雷达优化了机器学习推理的准确性。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审查标准进行评估，被认为值得支持。

项目成果

Fleet Active Learning: A Submodular Maximization Approach

• 发表时间: 2023
• 作者: Oguzhan Akcin;Orhan Unuvar;Onat Ure;Sandeep P. Chinchali

Decentralized Data Collection for Robotic Fleet Learning: A Game-Theoretic Approach

• 发表时间: 2022
• 作者: Oguzhan Akcin;Po-han Li;Shubhankar Agarwal;Sandeep P. Chinchali

Safe Networked Robotics With Probabilistic Verification

具有概率验证的安全网络机器人

• DOI: 10.1109/lra.2023.3340525
• 发表时间: 2024
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Narasimhan, Sai Shankar;Bhat, Sharachchandra;Chinchali, Sandeep P.
• 通讯作者: Chinchali, Sandeep P.