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: 2237577
• 负责人: Xiaobo Tan
• 金额: $ 39.69万
• 依托单位: Michigan State 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=2237577&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的快速发展极大地提高了感知能力，从而提高了机器的自主水平，过去十年自动驾驶汽车和无人机取得的巨大进步就是明证。这些成功归功于计算硬件和用于训练学习算法的大型数据集的进步。然而，对于许多真实世界的机器人应用，机器人的环境可能非常复杂，以至于现有的数据集都不足以满足要求，并且在仿真中合成生成高保真数据可能是不可能的。在这种情况下，机器人将需要在其真实的操作环境中收集数据以进行学习。机器人将需要有目的地规划其运动和与环境的交互，以使传感器能够收集最丰富的数据。该奖项支持研究创建有效的机器人主动学习算法，用于在高度动态和不确定的环境中感知和控制复杂系统，例如水环境。这些进步将对机器人技术的应用产生广泛的影响，例如水上碎片清理、水下搜索和救援以及个性化微创机器人手术。特别是，该小组将与美国海岸警卫队合作，并应用开发的算法来提高其搜索能力。 本项目的目标将通过三个相互关联的研究方向来实现：1）利用多传感器数据构建数据驱动感知模型的主动学习; 2）用于控制目的的描述感知特征时间演化的模型的主动学习，使用数据驱动算子来描述潜在动力学;以及3）使用配备有软传感器丰富的机械臂的无人驾驶水面车辆自主清除水中碎片的运行案例研究的实验演示和评估。这项工作将推进基于学习的感知模型的设计原则的基本理解时，涉及多种传感方式。该项目还将开发新的理论，用于学习潜在特征的演变，包括收敛保证和可控性分析。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。