CHS: Small: Watch One, Do One, Teach One: An Integrated Robot Architecture for Skill Transfer

在过去的几年里，机器人技术的研究已经从仅仅关注建造完全自主和有能力的机器人转变为包括建造与人类合作的部分能力的机器人，允许机器人做机器人最擅长的事情，而人类做人类最擅长的事情。 这种转变是由旨在加强中小规模制造业的安全、交互式系统的复兴推动的，同时也伴随着我们对机器人培训方式的改变。 机器人孤立运行的学习机制，从执行任务的人的被动观察中学习，正在被机器人通过与人类合作伙伴一起完成任务来学习的机制所取代。 该项目将寻求开发一种机器人架构，该架构允许由专家人类教练向机器人教授新技能，然后机器人成为与人类合作伙伴并肩作战的熟练合作者，最后机器人将学到的技能传授给新手人类学生。 为了实现这一目标，需要放弃流行但不透明的学习机制，转而采用新的表征，这种表征允许快速学习，同时在协作和教学过程中保持透明，并认真考虑人类合作伙伴的心理状态（知识，目标和意图）。 这项工作的一个基本成果将是一个统一的表示，连接现有的文献在学习从演示到协作的场景和场景涉及的机器人作为一个教练。因此，项目成果将在协同制造等应用领域产生广泛影响，同时也将加强我们在教育和培训方面的大量投资（特别是研究生和本科生研究人员的研究项目），并将进一步丰富扩大参与计算的努力。这一努力将建立在三个子领域的研究基础上，并扩展最先进的技术，以解决每个领域的不足：机器人作为学生 在从演示中学习的基础上，该团队将构建从人类学习任务模型的机器人。 然而，为了对其他重点领域有用，这些模型必须不像当前许多学习技术那样不透明。 相反，一个透明的模型将允许机器人提供和询问有关其性能的反馈，解释它学到了什么，并主动提出问题，以加快学习。 相对较新的人机协作领域致力于在人类和机器人伙伴之间同步任务执行。 通过链接到学习的任务行为模型和用户意图和理解模型，团队将构建系统，这些系统将能够熟练地协商任务分配，适应用户偏好，并在出现错误或更改计划时恢复/更新内部表示。 包括智能辅导系统在内的领域构建用户知识模型，通常使用贝叶斯知识跟踪建模。 然而，这些模型只是将知识显示为已知的、未知的或被遗忘的，并且只针对事实知识。 通过与任务和意图的具体表示相联系，该团队将创造出能够检测、扩展或修复学生心理模型的机器人，以完成现实世界的任务。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进行评估，被认为值得支持。

In the last several years, robotics research has transitioned from being concerned exclusively with building fully autonomous and capable robots to include building partially-capable robots that collaborate with human partners, allowing the robot to do what robots do best and the human to do what humans do best. This transition has been fueled by a renaissance of safe, interactive systems designed to enhance the efforts of small- and medium-scale manufacturing, and has been accompanied by a change in the way we think robots should be trained. Learning mechanisms in which the robot operates in isolation, learning from passive observation of people performing tasks, are being replaced by mechanisms where the robot learns through collaboration with a human partner as they accomplish tasks together. This project will seek to develop a robot architecture that allows for new skills to be taught to a robot by an expert human instructor, for the robot to then become a skilled collaborator that operates side-by-side with a human partner, and finally for the robot to teach that learned skill to a novice human student. To achieve this goal, popular but opaque learning mechanisms will need to be abandoned in favor of novel representations that allow for rapid learning while remaining transparent to explanation during collaboration and teaching, in conjunction with a serious consideration of the mental state (the knowledge, goals, and intentions) of the human partner. A fundamental outcome of this work will be a unified representation linking the existing literature in learning from demonstration to collaborative scenarios and scenarios involving the robot as an instructor. Thus, project outcomes will have broad impact in application domains such as collaborative manufacturing, while also enhancing our substantial investment in education and training (especially research offerings for graduate and undergraduate investigators), and will furthermore enrich the efforts to broaden participation in computing.This effort will build upon research in three subfields and extend the state-of-the-art to address deficiencies in each:1 - Robot as Student. Building on work from Learning from Demonstration, the team will construct robots that learn task models from humans. However, to be useful to the other thrust areas, these models must not be opaque as many current learning techniques are. Instead, a transparent model will allow the robot to provide and ask feedback about its performance, explain what it has learned, and to proactively ask questions that speed up learning.2 - Robot as Collaborator. The relatively new field of Human-Robot Collaboration struggles with synchronizing task execution between human and robot partners. By linking to models of learned task behavior and models of user intention and understanding, the team will construct systems that become proficient in negotiating task allocation, accommodating user preferences, and restoring/updating internal representations in case of errors or change of plans.3 - Robot as Teacher. Fields including Intelligent Tutoring Systems build models of user knowledge, typically modeled using Bayesian knowledge tracing. These models, however, simply show knowledge as known, unknown, or forgotten, and only for factual knowledge. By linking with concrete representations of task and intent, the team will create robots that can detect, extend, or repair the mental model of a student for real-world tasks.A set of milestones across three years will culminate in a demonstration of a robot that can learn a new task, collaborate on that task, and then teach that task to others.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.