Data Driven Predictive Auditory Cues for Safety and Fluency in Human-Robot Interaction

数据驱动的预测听觉线索可确保人机交互的安全性和流畅性

• 批准号: 2240525
• 负责人: Gil Weinberg
• 金额: $ 42.23万
• 依托单位: Georgia Tech Research Corporation
• 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: 2023-06-15 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240525&HistoricalAwards=false
• 关键词: Data; Driven; Predictive; Auditory; Cues

Most industrial and social robots are not sufficiently aware of their surroundings, which leads to a wide range of injuries. This can hamper fluent and efficient human-robot work. This project focuses on developing, integrating, and testing a set of sound cues for robotic movements, with the goals of enhancing human safety and allowing fluent interaction. The sound cues are created using algorithms which provide rich information about the robots’ current and future actions, alerting humans to potential hazards, and allowing them to prepare and adjust their work space. Such sound cues bear the promise of using aa non-distracting auditory channel to help humans plan their actions and responses to robotic actions. The system is based on music-driven robotic maneuvers using a novel audio generation method that will provide information about the robotic movements. The algorithm used is trained on a newly created dataset of audio clips with risk information. The system can increase safety, fluency and trust building in human-robot interaction in industrial and personal robots, private and public spaces, addressing tasks in manufacturing, training, education, and others.To address this goal the project is divided into four phases: Phase 1 - collection, analysis, labeling and feature extraction of a newly created dataset of audio clips. Phase 2 - development of a novel neural network model that will be trained on the collected dataset in correlation to labeled set of robotic movements. The output of the model will then be fed to a neural network that will generate long-context raw audio conditioned by musical and gestural features. Phase 3 - integration of the generated audio cues into a large set of robotic gestures with the goal of representing robotic motion and future actions. Phase 4 - a comprehensive evaluation study of the sonified robotic gestures for safety and fluency in a variety of human-robot interaction scenarios.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.

大多数工业和社交机器人无法充分了解周围环境，这会导致广泛的伤害，这可能会妨碍人类机器人流畅高效的工作。该项目专注于开发、集成和测试一组机器人声音提示。声音提示是使用算法创建的，该算法提供有关机器人当前和未来行为的丰富信息，提醒人类潜在危险，并允许他们准备和调整工作空间。 .这样的声音提示带有该系统基于音乐驱动的机器人操纵，使用一种新颖的音频生成方法，该方法将提供有关机器人运动的信息。该系统使用新创建的包含风险信息的音频片段数据集进行训练，可以提高工业和个人机器人、私人和公共空间中人机交互的安全性、流畅性和信任度，解决制造、培训、教育等领域的任务。 .为了实现这一目标，项目被划分分为四个阶段：第 1 阶段 - 新创建的音频剪辑数据集的收集、分析、标记和特征提取第 2 阶段 - 开发新颖的神经网络模型，该模型将在收集的数据集上与标记的机器人运动集相关进行训练。然后，模型的输出将被输入到神经网络，该神经网络将生成受音乐和手势特征调节的长上下文原始音频。第 3 阶段 - 将生成的音频提示集成到大量机器人手势中，以表示。机器人运动和未来行动阶段。 4 - 对可听机器人手势在各种人机交互场景中的安全性和流畅性进行的综合评估研究。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。