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.

为了使机器人能够与人类高效协作，在动作创建中必须考虑人类对其运动的感知。 由于人类协作者会解释机器人的运动（甚至是下意识的），因此不考虑人类观察者的机器人运动合成算法可能会创建被错误感知、负面解释（例如生气或威胁）或至少错过的运动。抓住机会有效地利用这种微妙的沟通渠道。该项目的关键思想是加深对人类运动感知的理解，并将其应用于机器人轨迹规划和控制算法的开发。该团队将利用人体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。该研究计划交织了对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 评估这些方法是否成功的情境部署。该项目的成功将使人们更深入地了解人们如何解释运动、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：受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人们。它将让更多具有交流能力的机器人成为老师和向导；更平易近人、更容易被接受的机器人，可以在家庭环境中工作，例如老年人护理；更多的协作机器人可以充当工人的助手；并且更容易指导可由非专家训练的机器人。该项目将通过将实证人体研究与机器人轨迹规划的技术挑战联系起来，加强 hte PI 的教育和推广工作。