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

近年来，机器学习和人工智能的快速发展极大地提高了机器的感知能力，从而提高了机器的自主水平，过去十年在自动驾驶汽车和无人机方面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，对于许多现实世界的机器人应用来说，机器人的环境可能非常复杂，以至于没有现有的数据集是足够的，并且在仿真中合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进行学习。机器人将需要有目的地计划其运动和与环境的交互，以使传感器能够收集最具信息量的数据。该奖项支持创建高效机器人主动学习算法的研究，以便在高度动态和不确定的环境(如水环境)中感知和控制复杂系统。机器人技术的进步将对机器人技术的应用产生广泛的影响，如清理水上碎片、水下搜救和个性化微创机器人手术。特别是，该团队将与美国海岸警卫队合作，并应用开发的算法来提高他们的搜索能力。这个项目的目标将通过追求三个相互关联的研究项目来实现：1)主动学习，利用多传感器数据建立数据驱动的感知模型；2)主动学习描述用于控制目的的感知特征的时间演变的模型，使用数据驱动的操作者描述潜在的动力学；3)实验演示和评价，包括使用配备了丰富软测量的机械臂的无人水面车辆自动清除水生碎片。这项工作将促进对涉及多个感知模式的基于学习的感知模型设计原则的基本理解。此外，该项目还将开发新的理论来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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.