CAREER: Optimal Experimental Design through Contact: Towards Robots that Plan to Learn

职业：通过接触进行最佳实验设计：迈向计划学习的机器人

• 批准号: 2238066
• 负责人: Ian Abraham
• 金额: $ 62.69万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238066&HistoricalAwards=false
• 关键词: CAREER; Optimal; Experimental; Design; through

The ability of humans to adapt in new environments and learn from a few interactions with their surroundings is long sought after in robotics. For example, when we step on ice, it only takes a few shuffles of our feet until we glide. Similarly, we are able to grasp and handle arbitrary objects with a few interactions. The closest robotics has achieved requires immense amounts of data, computation, and preexisting knowledge of the surroundings which pose significant barriers to obtain a glimpse of human-like capabilities. What if instead robots can plan for interactions that are beneficial for learning? Robots would have the potential to adapt to unseen and dynamically changing environments, broadening their utility in scenarios such as space exploration, deep ocean expeditions, and in humanitarian services like search and rescue. This Faculty Early Career Development (CAREER) grant seeks to develop these capabilities for robots to quickly adapt and learn new manipulation and locomotion skills by planning to learn through intentional interactions. Project activities include a synergistic educational and outreach plan in line with the PI’s goal of a well-rounded education for underrepresented, veteran, and English as a Second Language (ESL) students in robotics. This plan includes a curriculum for integrating theoretical and practical courses in robotics, an educational program for literacy in science in diverse languages for ESL students, and undergraduate research opportunities for veterans. This project will support the PI’s goal of educating the next generation of diverse, inclusive, and capable roboticists. Motivated by how humans learn with just a few interactions, this project will advance how robots optimize and plan informative interactions for quickly learning locomotion and manipulation skills. The approach is centered on optimizing planned contact interactions that allow robots to take an active role in learning. The project plans an optimal experimental design formulation for reasoning about motion, contact interactions, and learning outcomes as a unifying optimal control problem. The effects of modeling choices in the planned formulation will be investigated. In addition, an online, model-predictive control (MPC) approach will be developed using repeatable dynamic learning primitives for real-time, deterministic, and reproducible learning behaviors. Furthermore, this project will demonstrate guarantees of reproducibility and certified learning as a result of robots influencing their learning outcomes through motion.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人类适应新环境的能力，以及从与周围环境的一些互动中学习的能力，长期以来一直是机器人领域所追求的。例如，当我们踩在冰上时，只需要几次洗牌，我们的脚，直到我们滑行。同样，我们能够通过一些交互来抓住和处理任意对象。最接近的机器人技术需要大量的数据、计算和预先存在的环境知识，这对获得类似人类的能力构成了重大障碍。如果机器人可以计划有利于学习的互动呢？机器人将有潜力适应看不见的和动态变化的环境，扩大它们在太空探索、深海探险以及搜索和救援等人道主义服务中的用途。这个教师早期职业发展（CAREER）补助金旨在通过计划通过有意的互动学习，为机器人开发这些能力，以快速适应和学习新的操纵和运动技能。项目活动包括协同教育和推广计划，符合PI的目标，即为代表性不足，退伍军人和英语作为第二语言（ESL）的学生提供全面的机器人教育。该计划包括整合机器人理论和实践课程的课程，ESL学生不同语言的科学素养教育计划，以及退伍军人的本科研究机会。该项目将支持PI的目标，即教育下一代多样化，包容性和有能力的机器人专家。受人类通过少量互动学习的启发，该项目将推进机器人如何优化和规划信息交互，以快速学习运动和操作技能。该方法的核心是优化计划的接触互动，使机器人在学习中发挥积极作用。该项目计划一个最佳的实验设计公式推理运动，接触的相互作用和学习结果作为一个统一的最优控制问题。将研究计划配方中模型选择的影响。此外，一个在线的，模型预测控制（MPC）的方法将开发使用可重复的动态学习原语的实时，确定性和可再生的学习行为。此外，该项目还将展示机器人通过运动影响学习结果的可重复性和认证学习的保证。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估而被认为值得支持。