FRR: Collaborative Research: Unsupervised Active Learning for Aquatic Robot Perception and Control

FRR：协作研究：用于水生机器人感知和控制的无监督主动学习

• 批准号: 2237576
• 负责人: Todd Murphey
• 金额: $ 41.16万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237576&HistoricalAwards=false
• 关键词: FRR; Collaborative; Research; Unsupervised; Active

Rapid developments in machine learning and artificial intelligence in recent years have greatly advanced perception capabilities and thus the level of autonomy for machines, as evidenced by great strides made in autonomous vehicles and aerial drones over the last decade. These successes are due to advances in computing hardware and large datasets for training learning algorithms. However, for many real-world robotic applications, a robot’s environment may be so complex that no existing datasets are adequate, and synthetically generating high-fidelity data in simulation may not be possible. In such cases a robot will need to collect data in its real operating environment to learn. The robot will need to purposefully plan its motion and interaction with the environment to enable sensors to gather the most informative data. This award supports research to create algorithms for efficient robot active learning for perception and control of complex systems in highly dynamic and uncertain environments, such as the aquatic environment. Advances will have broad implications in applications of robotic technologies, such as aquatic debris cleanup, underwater search and rescue, and personalized minimally invasive robotic surgery. In particular, the team will collaborate with the United States Coast Guard and apply the developed algorithms to improve their search capacities. The goal of this project will be accomplished through the pursuit of three interconnected research thrusts: 1) active learning for building data-driven perception models with multi-sensory data; 2) active learning of models describing temporal evolution of perceptional features for control purposes, using data-driven operators to describe latent dynamics; and 3) experimental demonstration and evaluation with a running case study of autonomous aquatic debris removal using an unmanned surface vehicle equipped with soft sensor-rich robotic arms. This work will advance the fundamental understanding of design principles for learning-based perception models when multiple sensing modalities are involved. The project will moreover develop new theory for learning the evolution of latent features, including convergence guarantees and controllability analysis.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.

近年来，机器学习和人工智能方面的快速发展具有出色的高级感知能力，因此机器的自主权水平是在过去十年中在自动驾驶汽车和空中无人机中取得的巨大进步所证明的。这些成功归因于计算硬件和用于培训学习算法的大型数据集的进步。但是，对于许多现实世界的机器人应用程序，机器人的环境可能是如此复杂，以至于没有现有数据集是足够的，并且在仿真中综合生成高保真数据可能是不可能的。在这种情况下，机器人将需要在其实际操作环境中收集数据才能学习。机器人将需要有目的地计划其与环境的运动和互动，以使传感器能够收集最有用的数据。该奖项支持研究，以创建有效的机器人主动学习算法，以感知和控制高度动态和不确定的环境（例如水生环境）的复杂系统。进步将对机器人技术的应用有广泛的影响，例如水生碎片清理，水下搜索和救援以及个性化的微创机器人手术。特别是，该团队将与美国海岸警卫队合作，并应用发达的算法以提高其搜索能力。该项目的目标将通过追求三个相互联系的研究推力来实现：1）积极学习，用于构建具有多感觉数据的数据驱动的感知模型； 2）使用数据驱动的操作员描述潜在动态的模型，描述了用于控制目的的感知特征的临时演变模型； 3）使用配备有软传感器的机器人臂的无人层面车辆对自动水生碎片去除的运行案例研究进行了实验演示和评估。当涉及多种灵敏度方式时，这项工作将提高对基于学习的感知模型的设计原理的基本理解。此外，该项目将开发新的理论，以学习潜在特征的演变，包括融合保证和可控性分析。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准来通过评估被认为是珍贵的支持。