CAREER: Robot Learning of Complex Tasks via Skill Reusability and Refinement

能够在非结构化环境中执行复杂操作任务的机器人的创造将开辟新的应用，帮助人们在家中、在工作中和在社会中影响和提高生活质量。机器人学习方面的现有工作使机器人能够复制非常简单的操作任务。然而，学习复杂的操作任务来帮助人们(例如，装洗碗机、去杂货店购物、换灯泡)需要算法上的进步。该项目将贡献新的理论方法和算法，将推动机器人学习的最先进水平，并加速机器人的开发和采用，能够支持具有各种辅助和自主应用的人。此外，这个项目将通过开发新课程、指导和支持来自代表性不足群体的学生、接触K-12学生和普通公众以及组织教育工作坊来整合研究、教育和推广。该项目的目标是为学习和推广复杂的操作任务开发一个统一的框架。我们的框架利用人与机器人的交互和向人类学习的方法来解决几个现有的挑战：能够建模并从人类例子中学习原始技能的机器人大多忽略了类似人类的运动的特征。构建复杂任务计划所需的所有个人技能必须先验建模，并且不能在此过程中被发现。随着任务条件的变化，学习到的技能不能有效地适应或提炼，需要重新调整。为了加深对这些领域的了解，本项目侧重于：(A)通过将基本概念和经过实验证明的人体运动数学模型整合到现有学习框架中，建立建模原始技能的统一形式化方法；(B)开发在任务学习过程中发现一系列复杂操作技能的共同和可重复使用的基本基的方法；(C)创建具有很强适应性的新颖技能精炼方法，用于分解和重构复杂任务，这些方法可受益于多模式人类反馈和其他与任务相关的背景，同时计算成本较低。我们研究思路的潜在变革性方面包括：为原始技能的数学建模带来新的视角，为技能改进和可重复使用的技能发现问题找到实用的解决方案，并揭示复杂操作任务学习的完整框架的发展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The creation of robots capable of performing complex manipulation tasks in unstructured environments will open up new applications for helping people in their homes, at work, and in society to impact and enhance quality of life. Existing work in robot learning has enabled robots to replicate very simple manipulation tasks. However, learning complex manipulation tasks to assist people (e.g., loading a dishwasher, grocery shopping, changing a lightbulb) demands algorithmic advancements. This project will contribute new theoretical methods and algorithms that will advance the state-of-the-art in robot learning and accelerate the development and adoption of robots capable of supporting people with a variety of assistive and autonomous applications. In addition, this project will integrate research, education, and outreach by developing new courses, mentoring and supporting students from underrepresented groups, reaching out to K-12 students and the general public, and organizing educational workshops.The goal of this project is to develop a unified framework for learning and generalization of complex manipulation tasks. Our framework leverages human-robot interaction and learning-from-human approaches to address several existing challenges: Robots that can model and learn primitive skills from human examples mostly ignore characteristics of human-like movements. All the individual skills needed to construct a complex task plan must be modeled a priori and cannot be discovered during the process. As the task conditions change, the learned skills cannot be adapted or refined effectively and need to be remodeled. To further knowledge in these areas, this project focuses on: (a) building a unifying formalization for modeling primitive skills by integrating fundamental concepts and experimentally proven mathematical models of human movements into existing learning frameworks; (b) developing approaches for discovering common and reusable primitive basis for a family of complex manipulation skills during the task learning process; (c) creating novel skill refinement methods with strong adaptability properties utilized for the decomposition and reconstruction of complex tasks that can benefit from multi-modal human feedback and other task-related context, while being computationally inexpensive. The potentially transformative aspects of our research ideas include: bringing a new perspective to the mathematical modeling of primitive skills, finding practical solutions to the problems of skill refinement and reusable skill discovery, and shedding light on the development of a complete framework for complex manipulation task learning.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.