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

人类适应新环境并从与周围环境的一些互动中学习的能力一直是机器人技术所追求的。例如，当我们踩在冰上时，我们的脚只需要挪动几下就能滑行。类似地，我们能够通过一些交互来掌握和处理任意对象。机器人技术所取得的最接近的成就需要大量的数据、计算和对周围环境的预先存在的知识，这对获得类人能力的一瞥构成了重大障碍。如果机器人可以计划对学习有益的互动呢？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。这项教师早期职业发展（Career）资助旨在通过计划学习，通过有意的互动来发展机器人快速适应和学习新的操作和运动技能的能力。项目活动包括协同教育和推广计划，以符合PI的目标，即为代表性不足的退伍军人和英语为第二语言（ESL）的学生提供全面的机器人教育。该计划包括机器人理论和实践课程的整合课程，为ESL学生提供多种语言的科学素养教育计划，以及为退伍军人提供本科研究机会。该项目将支持PI的目标，即培养下一代多样化、包容性和有能力的机器人专家。受人类如何通过少量交互学习的启发，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。该方法以优化计划接触交互为中心，使机器人在学习中发挥积极作用。该项目计划将运动、接触相互作用和学习结果作为统一的最优控制问题进行推理的最佳实验设计公式。模型选择对计划配方的影响将被研究。此外，将开发一种在线模型预测控制（MPC）方法，使用可重复的动态学习原语来实现实时、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结果，该项目将展示可重复性和认证学习的保证。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.