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

职业：机器人通过技能的可重用性和改进来学习复杂的任务

• 批准号: 2237463
• 负责人: Reza Ahmadzadeh
• 金额: $ 49.92万
• 依托单位: University of Massachusetts Lowell
• 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: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237463&HistoricalAwards=false
• 关键词: CAREER; Robot; Learning; Complex; Tasks

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.

创造能够在非结构化环境中执行复杂操作任务的机器人将开辟新的应用，帮助人们在家庭、工作和社会中影响和提高生活质量。机器人学习的现有工作使机器人能够复制非常简单的操作任务。然而，学习复杂的操纵任务来帮助人们（例如，装洗碗机、买杂货、换灯泡）都需要算法的进步。该项目将提供新的理论方法和算法，推动机器人学习的最新发展，并加速机器人的开发和采用，这些机器人能够支持人们进行各种辅助和自主应用。此外，该项目还将通过开发新课程、指导和支持来自代表性不足群体的学生、接触K-12学生和公众以及组织教育研讨会，整合研究、教育和推广。该项目的目标是开发一个统一的框架，用于学习和推广复杂的操作任务。我们的框架利用人机交互和向人类学习的方法来解决几个现有的挑战：可以从人类示例中建模和学习原始技能的机器人大多忽略了类人运动的特征。构建复杂任务计划所需的所有个人技能都必须事先建模，并且不能在过程中发现。随着任务条件的变化，学习到的技能不能有效地适应或改进，需要重新塑造。为了进一步了解这些领域的知识，本项目的重点是：（a）通过将基本概念和实验证明的人体运动数学模型整合到现有的学习框架中，建立一个统一的形式化模型，用于建模原始技能;（B）开发在任务学习过程中为一系列复杂操作技能发现共同的和可重用的原始基础的方法;（c）创建具有强适应性特性的新颖技能细化方法，用于分解和重构复杂任务，这些复杂任务可以受益于多模态人类反馈和其他与任务相关的上下文，同时计算成本低廉。我们的研究思路的潜在变革方面包括：为原始技能的数学建模带来了新的视角，为技能细化和可重用技能发现的问题找到了切实可行的解决方案，该奖项反映了NSF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。