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.

该建议通过考虑个体生理的约束来解决人机交互的建模和控制问题。这个项目的动机是在非结构化环境中对自动化的需求不断增加，这些环境需要高级的认知处理和复杂的决策，而这些还不能完全自动化。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交互模型中，以解释个体差异。每个合作活动被划分为需要不同控制策略的动作原语，同时从不同的传感器估计人的意图。该框架基于混合系统理论，提供了可证明的安全性和稳定性准则。这项研究的结果将促进更安全、更可靠的人机交互方法，并推动人类运动分析和控制理论的发展。这项研究的广泛影响将通过对人类意图的理解和适用于一般人机交互的触觉合作的新见解来实现。随着人们对服务机器人的兴趣日益浓厚，安全可靠的交互将成为机器人在人类居住环境中成功引入的关键。由于生产率的提高和成本的降低，机器人与人类一起从事服务业和制造业的潜在经济影响是巨大的。另一个新兴领域是康复和辅助机器人。开发的数据驱动的肌肉骨骼模型也将适用于医疗保健和人体工程学中身体损伤的量化和肌肉压力的估计。这项跨学科研究为本科生和研究生提供了绝佳的机会，让他们参与分析挑战、理论结果的实验室演示和实验评估。