Fast and Reliable Online Retraining and Adaptation for Robot Planning Despite Missing World Knowledge

尽管缺少世界知识，但仍能快速可靠地对机器人规划进行在线再培训和适应

基本信息

批准号：
2232733
负责人：
Gregory Stein
金额：
$ 49.95万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2232733&HistoricalAwards=false
关键词：
Fast Reliable Online Retraining Adaptation

项目摘要

The next generation of service robots will be required to act in unfamiliar and ever-changing environments. At the request of human operators, such robots will be expected to reliably complete complex objectives despite missing or out-of-date information about their surroundings: locating key places, delivering supplies, and finding personnel, even when they are uncertain where to look. While machine learning has proven an important component of good behavior in this domain, learning-driven strategies can be brittle to change, resulting in poor performance in new or unfamiliar environments with little recourse to improve without significant downtime and supervision. Poor performance begets mistrust, limiting the adoption of service robots and thus their potential to provide autonomy or assistance to human operators in settings ranging from homes to hospitals. This project aims to overcome these limitations through development of an approach for service robot decision-making designed to allow state-of-the-art performance in challenging, unfamiliar environments and facilitate fast and reliable improvement during deployment. Our contributions will allow for more performant, reliable, and trustworthy robots capable of good behavior in unstructured environments. One key aspect of our work will facilitate non-expert users to quickly correct robot behavior, a capability unique to our proposed approach in this domain that will help democratize robot training, a step towards more trustworthy and ethical robots. Moreover, our advancements will help to lower the barrier to entry for student engagement with robotics and machine learning and our research program is integrated with educational initiatives that engage both undergraduates and students from underrepresented groups from D.C. area high schools. Our project will develop a principled approach for improving robot behavior during deployment for long-horizon planning in partially-mapped environments, emphasizing reliability, data efficiency, and performance. We will demonstrate that the coupling of data-driven (learning-informed) and classical (STRIPS-style) planning afforded by our abstraction will be a key enabler of this advance; learning will augment model-based planning, allowing completeness and introspection despite missing knowledge. Our robot will rely on two complementary sources of information: (i) online experience, in which the robot uses data it collects during deployment to self-audit and to retrain and adapt its learned behaviors, and (ii) expert guidance, in which an environment expert (e.g., a robot or human auditor) intervenes to prompt a change in long-horizon behavior. Our project will build upon recent progress in planning under uncertainty, trustworthy AI, robust planning, and domain adaptation, and therefore has the potential to advance the state-of-the-art in multiple areas at once. We will demonstrate both simulated and real-world experiments in which a mobile manipulator robot must navigate large-scale, unfamiliar home- and hospital-like buildings to complete complex multi-stage tasks involving locating key places, interacting with the environment, and retrieving objects and persons. We intend to both theoretically justify and demonstrate empirically the utility of our proposed approach to quickly and reliably improve deployment-time performance for a variety of service robot tasks.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.

下一代服务机器人将需要在不熟悉和不断变化的环境中行动。应人类操作员的要求，尽管缺少或过时的信息，但即使在不确定的地方不确定，这些机器人仍将可靠地完成复杂的目标：找到关键地方，提供物资和寻找人员。尽管机器学习已经证明了该领域良好行为的重要组成部分，但学习驱动的策略可能会变化，导致在新的或不熟悉的环境中的性能不佳，而没有大量停机时间和监督，但几乎没有改进。性能不佳会产生不信任，限制了服务机器人的采用，因此在从房屋到医院的环境中为人类运营商提供自治或援助的潜力。该项目的目的是通过开发一种旨在允许在具有挑战性，陌生的环境中允许最先进的绩效的服务机器人决策方法来克服这些局限性，并促进部署过程中快速，可靠的改进。我们的贡献将允许在非结构化环境中具有良好行为的性能，可靠，可信赖的机器人。我们工作的一个关键方面将促进非专家用户快速纠正机器人行为，这是我们在该领域提出的方法所特有的能力，这将有助于使机器人培训民主化，这是朝着更加值得信赖和道德的机器人迈出的一步。此外，我们的进步将有助于降低学生参与机器人和机器学习的进入的障碍，而我们的研究计划与教育计划集成了，这些计划与来自华盛顿特区地区高中的代表性不足的团体的大学生和学生都相结合。我们的项目将开发一种有原则的方法，用于在部分映射环境中为长摩根计划的部署过程中改善机器人行为，从而强调可靠性，数据效率和性能。我们将证明，通过我们的抽象提供的数据驱动（学习知觉）和经典（脱衣舞风格）计划的耦合将是这一进步的关键推动者；学习将增强基于模型的计划，尽管知识缺失，但仍可以完整和内省。我们的机器人将依靠两种补充信息来源：（i）在线体验，其中机器人使用其在部署期间收集的数据进行自我审核并重新训练和调整其学习的行为，以及（ii）专家指导，其中环境专家（例如，机器人或人类审核员）中间会促使长期疗程的变化。我们的项目将基于不确定性，值得信赖的AI，健壮的计划和领域适应的最新计划进展，因此有可能立即推进多个领域的最先进。我们将展示模拟的和现实世界的实验，其中移动操纵器机器人必须在大规模，陌生的家庭和类似医院的建筑物中浏览，以完成涉及查找关键地方，与环境互动以及检索物体和人员互动的复杂多阶段任务。我们打算从理论上证明并证明我们提出的方法的实用性快速，可靠地改善了各种服务机器人任务的部署时间绩效。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。