NRI-Small: Perceptually Inspired Dynamics for Robot Arm Motion

NRI-Small：机器人手臂运动的感知动力学

• 批准号: 1208632
• 负责人: Michael Gleicher
• 金额: $ 79.99万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208632&HistoricalAwards=false
• 关键词: NRI; Small; Perceptually; Inspired; Dynamics

In order for robots to collaborate efficiently and effectively with humans, the human perception of their movement must be considered in motion creation. Because a human collaborator will interpret the movements of a robot (even subconsciously), robot motion synthesis algorithms that do not consider the human observer may create motions that are perceived incorrectly, interpreted negatively (e.g. as being angry or threatening), or at least miss out on the opportunity to use this subtle communication channel effectively. The key idea of this project is to develop an understanding of human perception of movement that can be applied to the development of robot trajectory planning and control algorithms. The team will use human subjects experiments to understand and evaluate the interpretation of movements and apply these findings in robotics and motion synthesis. The research plan interleaves empirical studies of how people interpret motions, algorithm development to create methods that generate robot motions in a controllable manner, and contextualized deployments that allow the PIs to evaluate the success of the methods. The success of the project will provide a deeper understanding of how people interpret movements, new algorithms for synthesizing robot movements, and demonstrations of the potential applications of collaborative robots.Broader Impact: Perceptually inspired robot motion synthesis algorithms will enable robots to collaborate more effectively with people. It will enable more communicative robots that can serve as teachers and guides; more approachable and acceptable robots that can work in domestic situations such as elder care; more cooperative robots that can work as assistants to workers; and easier to instruct robots that can be trained by non-experts. This project will enhance the education and outreach efforts of hte PIs by connecting empirical human studies to the technical challenges of robot trajectory planning.

为了使机器人能够有效地与人类协作，在运动创建中必须考虑人类对其运动的感知。 因为人类合作者会解释机器人的动作（甚至是下意识的），所以不考虑人类观察者的机器人动作合成算法可能会产生被错误感知的动作，被负面解释（例如愤怒或威胁），或者至少错过了有效使用这种微妙的通信渠道的机会。该项目的主要思想是发展人类对运动的感知的理解，这种理解可以应用于机器人轨迹规划和控制算法的开发。该团队将使用人类受试者实验来理解和评估运动的解释，并将这些发现应用于机器人和运动合成。该研究计划交织了人们如何解释运动的实证研究，算法开发以创建以可控方式生成机器人运动的方法，以及允许PI评估方法成功的上下文部署。该项目的成功将使人们更深入地了解人们如何理解运动，合成机器人运动的新算法，以及协作机器人潜在应用的演示。更广泛的影响：感知启发的机器人运动合成算法将使机器人能够更有效地与人协作。它将使更多的机器人能够充当教师和向导;更容易接近和接受的机器人，可以在家庭环境中工作，如老人护理;更合作的机器人，可以作为工人的助手;更容易指导机器人，可以由非专家训练。该项目将通过将经验性人类研究与机器人轨迹规划的技术挑战联系起来，加强专业人员的教育和推广工作。