NRI: Collaborative Research: Human-Centered Modeling and Control of Cooperative Manipulation with Bimanual Robots

NRI：协作研究：以人为中心的双手机器人协作操纵建模与控制

• 批准号: 1427396
• 负责人: Oussama Khatib
• 金额: $ 60万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1427396&HistoricalAwards=false
• 关键词: NRI; Collaborative; Research; Human; Centered

This proposal addresses modeling and control aspects of human-robot interaction by considering constraints imposed by an individual's physiology. The project is motivated by increasing demand for automation in unstructured environments that require high-level cognitive processing and complex decision-making which cannot yet be fully automated. By taking human-centric approach, data-driven musculoskeletal models are incorporated into the robot interaction model to account for differences of individuals. Each cooperative activity is divided into action primitives requiring different control strategies while estimating human intent from various sensors. The framework is based on theory of hybrid systems that provides provable safety and stability criteria. The outcome of this research will facilitate methodology for safer and more reliable human-robot interaction and advance state-of-the-art in human movement analysis and control theory. The broader impacts of this research will be realized through new insights into understanding of human intent and haptic cooperation applicable to general human-machine interaction. With increasing interest in service robotics safe and reliable interaction will be the key to successful introduction of robots in human-occupied environments. The potential economic impact of robots engaged in services and manufacturing alongside humans are significant due to increased productivity and reduced costs. Another emerging area is rehabilitation and assistive robotics. The developed data-driven musculoskeletal models will also be applicable to quantification of physical impairments and estimation of muscular stress in healthcare and ergonomics. This interdisciplinary research provides excellent opportunities for undergraduate and graduate students to be engaged in analytical challenges, laboratory demonstrations of theoretical results, and experimental evaluations.

该建议通过考虑个人生理学施加的约束来解决人类机器人相互作用的建模和控制方面。该项目是由于需要高级认知处理和复杂的决策制定的非结构化环境中自动化的需求增加而动机。通过采用以人为中心的方法，将数据驱动的肌肉骨骼模型纳入机器人相互作用模型中，以解释个人的差异。每个合作活动都分为需要不同控制策略的动作原始，同时估算各种传感器的意图。该框架基于提供可证明的安全性和稳定标准的混合系统理论。这项研究的结果将促进人类运动分析和控制理论中更安全，更可靠的人类机器人相互作用的方法论。这项研究的更广泛的影响将通过了解适用于一般人机相互作用的人类意图和触觉合作的新见解来实现。随着对服务机器人技术的兴趣日益增加，安全可靠的互动将是成功引入人类占领环境中的机器人的关键。由于生产率提高和成本降低，与人类一起从事服务和制造的机器人的潜在经济影响很大。另一个新兴地区是康复和辅助机器人技术。开发的数据驱动的肌肉骨骼模型也将适用于量化身体障碍和医疗保健和人体工程学中肌肉应激的估计。这项跨学科研究为大学生和研究生提供了极大的机会，以应对分析挑战，实验室的理论结果和实验评估。