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.

在机器人技术中，人类在新环境中适应新环境并从与周围环境中学习的能力很长。例如，当我们踏上冰上时，它只需要几次脚步，直到我们滑行。同样，我们能够通过一些交互来掌握和处理任意对象。最接近的机器人技术需要大量的数据，计算和对周围环境的知识，这些知识构成了明显的障碍，以了解人类样能力。如果相反，机器人可以计划有利于学习的互动怎么办？机器人将有可能适应看不见且动态变化的环境，在太空探索，深海探险等场景中扩大效用，以及在搜索和救援等人道主义服务中的效用。这位教师早期职业发展（职业）赠款旨在通过计划通过有意的互动来学习新的操纵和运动技能来开发这些能力，以供机器人快速适应和学习新的操纵和运动能力。项目活动包括根据PI的目标，即对机器人技术的第二语言（ESL）学生进行全面教育的目标，这是一致的协同教育和外展计划。该计划包括一项课程，用于整合机器人技术的理论和实用课程，这是一项针对ESL学生的不同语言科学识字课程，以及对退伍军人的本科研究机会。该项目将支持PI的目标，即教育下一代潜水员，包容和能力的机器人主义者。由于人类如何仅通过一些互动学习，该项目将推动机器人如何优化和计划信息互动，以快速学习运动和操纵技巧。该方法以优化的计划接触相互作用为中心，该互动使机器人能够在学习中发挥积极作用。该项目计划了一个最佳的实验设计公式，用于推理运动，接触互动和学习成果，作为统一的最佳控制问题。将研究计划中公式中建模选择的影响。此外，将使用可重复的动态学习基础来开发一种在线模型预测控制方法（MPC）方法，以实时，确定性和可重复的学习行为。此外，由于机器人通过运动影响其学习成果，该项目将证明保证可重复性和认证学习。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响来审查标准的评估，被认为是宝贵的支持。