Towards Robust and Natural Underwater Human-Robot Interaction

水下领域几乎占据了地球的五分之四，对人类的探索有着天生的敌意。然而，在海洋环境中的许多应用中（例如，在监视、环境监测、安全和搜索和救援中），人的评估对于高效和有效地完成任务是必要的。目前的水下勘探技术看到了自主水下航行器（AUV）的有限应用，但严重依赖于远程操作的车辆，不幸的是，这并没有利用机器人的自主性。该项目将开发新型算法和协议，使人类能够与AUV安全通信，同时保留和利用其自主性。具体而言，其目的是创建基于手势和运动的双向人机通信方法的新方法，并使自主水下机器人能够检测，识别和与特定个体进行交互。研究目标将被单独评估，并作为一个综合的，连贯的系统机载水下航行器。拟议的研究有可能在人在回路领域创造一个全新的方向，水下机器人伴侣能够协助潜水员完成一系列任务，甚至学习以自主方式执行这些任务，大大降低人类的风险。这项研究还将影响广泛的学科，包括人机对话，机器视觉，活动识别和机器人控制。这项研究将开发新的算法和协议，使人类能够安全地与AUV进行通信，同时保持和利用其自主性。具体目标包括：开发一种具有多种通信粒度的基于手势的人与机器人语言;通过从空间和周期性线索中学习，创建用于人类视觉识别的算法;以及开发一种非语言的、基于运动的水下机器人与人通信方案。 研究目标将被单独评估，并作为一个综合的，连贯的系统机载水下航行器。 对基于手势的视觉语言的调查将量化这些方法在现实环境中的可检测性、可用性和有效性。基于统计、卷积和生成学习的方法将被应用于理解可识别的人类特征和手势通信。此外，机器人的肢体语言和动作将被用作机器人与人类非语言交流的线索。这些方法将通过用户研究和机器人现场试验进行定量和定性验证，以表征其优点和局限性。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。

The underwater domain takes up almost four-fifths of the planet and is inherently hostile towards human exploration. However, in numerous applications in the marine environment (e.g., in surveillance, environmental monitoring, security, and search-and-rescue), human assessment is necessary for efficient and effective task completion. Current technology for underwater exploration sees limited applications of autonomous underwater vehicles (AUVs) but relies heavily on remotely operated vehicles, which unfortunately does not take advantage of robot autonomy. This project will develop novel algorithms and protocols to enable humans to communicate safely with AUVs while preserving and leveraging their autonomy. Specifically, the intent is to create novel methods for gesture- and motion-based bidirectional human-robot communication methods and enable autonomous underwater robots to detect, identify and interact with specific individuals. The research objectives will be evaluated individually and as an integrated, coherent system onboard underwater vehicles. The proposed research has the potential to create a fundamentally new direction in human-in-the-loop field robotics, with underwater robot companions being able to assist divers in a range of tasks and even learning to carry out these tasks in an autonomous manner, greatly reducing risk to humans. This research will also impact a broad range of disciplines, including human-machine dialog, machine vision, activity recognition, and robot control.This research will develop novel algorithms and protocols to enable humans to communicate safely with AUVs while preserving and leveraging their autonomy. Specific goals include: development of a gesture-based human-to-robot language with multiple communication granularities; creation of algorithms for visual identification of humans by learning from spatial and periodic cues; and development of a non-verbal, motion-based underwater robot-to-human communication scheme. The research objectives will be evaluated individually and as an integrated, coherent system onboard underwater vehicles. The investigation into gesture-based visual languages will quantify the detectability, usability, and efficacy of such methods in realistic settings. Statistical, convolutional and generative learning-based approaches will be applied to understand both identifiable human features and gestural communication. Additionally, robot body language and motion will be used as cues for robot-to-human non-verbal communication. These methods will be quantitatively and qualitatively validated via user studies and robot field trials to characterize their advantages and limitations.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.