NRI-Small: Expert-Apprentice Collaboration

NRI-Small：专家与学徒的合作

• 批准号: 1208245
• 负责人: Carlo Tomasi
• 金额: $ 74.69万
• 依托单位: Duke University
• 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: 2012-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208245&HistoricalAwards=false
• 关键词: NRI; Small; Expert; Apprentice; Collaboration

Recent advances in robot platforms have outpaced our ability to effectively program robots to accomplish useful tasks, often in complex environments that they share with humans. In order for flexible, general purpose robots to become widespread e.g., in teaching skills to children, assisting the elderly, there must be a way of interacting with them beyond programming. Teaching by demonstration offers a potentially powerful and practical approach to realizing the promise of large scale personal robotics in a wide range of applications. In teaching by demonstration, the expert (human), demonstrates the task on different hardware than what the apprentice or student (robot) uses. The project aims to develop visual feature-based methods that allow robots to teach humans and learn from them by unifying apprenticeship learning, learning by demonstration (or by imitation) and teaching humans, taking into account the differences between experts and apprentices. The resulting system will be evaluated on a PR2 robot (mostly on grasping and manipulation tasks). The scientific advances resulting from the project in learning from demonstration and imitation learning, both general techniques with broad applicability, will greatly simplify the programming of robots which would make it easier for non-expert users to perform this important task which currently requires considerable expertise in robotics as well as computer science. Broader impacts of the research include development of new robotics curricula, enhanced opportunities for research-based interdisciplinary training at the intersection of computer vision, machine learning, and robotics, outreach activities (including participation in a public school robotics instruction program). All of the results of the research, including publications, open-source software and datasets, will be made freely available to the larger scientific and academic community.

机器人平台的最新进展已经超过了我们对机器人进行有效编程以完成有用任务的能力，通常是在与人类共享的复杂环境中。 为了使灵活的通用机器人变得广泛，例如，在向儿童传授技能、帮助老年人时，必须有一种与他们互动的方式，而不仅仅是编程。演示教学提供了一个潜在的强大的和实用的方法来实现大规模的个人机器人在广泛的应用前景。在演示教学中，专家（人类）在与学徒或学生（机器人）使用的不同硬件上演示任务。该项目旨在开发基于视觉特征的方法，使机器人能够通过统一学徒学习，通过演示（或模仿）学习和教授人类来教授人类并向人类学习，同时考虑到专家和学徒之间的差异。由此产生的系统将在PR2机器人上进行评估（主要是抓取和操作任务）。该项目在从示范和模仿学习中学习方面取得的科学进步，这两种通用技术具有广泛的适用性，将大大简化机器人的编程，使非专家用户更容易执行这一重要任务，目前需要相当多的机器人和计算机科学方面的专业知识。该研究的更广泛影响包括开发新的机器人课程，在计算机视觉，机器学习和机器人技术的交叉点增加基于研究的跨学科培训机会，推广活动（包括参与公立学校机器人教学计划）。所有研究成果，包括出版物、开源软件和数据集，都将免费提供给广大的科学和学术界。