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

• 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

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the request of human operators, such robots will be expected to reliably complete complex objectives despite missing or out-of-date information about their surroundings: locating key places, delivering supplies, and finding personnel, even when they are uncertain where to look.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被要求在不熟悉和不断变化的环境中工作。在人类操作员的要求下，这些机器人将有望可靠地完成复杂的目标，尽管缺少或过时的周围信息：定位关键地点，运送物资和寻找人员，即使他们不确定该去哪里寻找。虽然机器学习已被证明是这一领域良好行为的重要组成部分，但学习驱动的策略可能很难改变，导致在新的或不熟悉的环境中表现不佳，在没有显著停机和监督的情况下几乎没有改善的途径。性能不佳会引发不信任，限制了服务机器人的采用，从而限制了它们在从家庭到医院的各种环境中为人类操作员提供自主或协助的潜力。该项目旨在通过开发一种服务机器人决策方法来克服这些限制，该方法旨在在具有挑战性的陌生环境中实现最先进的性能，并在部署过程中促进快速可靠的改进。我们的贡献将允许更高性能，可靠和值得信赖的机器人能够在非结构化环境中表现良好。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，这是我们在这一领域提出的方法所独有的能力，将有助于使机器人培训民主化，这是迈向更值得信赖和道德的机器人的一步。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门槛，我们的研究计划与教育计划相结合，吸引来自华盛顿特区代表性不足群体的本科生和学生。我们的项目将开发一种原则性的方法，用于在部分映射环境中进行长期规划的部署过程中改善机器人行为，强调可靠性，数据效率和性能。我们将证明，我们的抽象所提供的数据驱动（学习知情）和经典（STRIP风格）规划的耦合将是这一进步的关键推动因素;学习将增强基于模型的规划，尽管缺少知识，但仍允许完整性和内省。我们的机器人将依赖于两个互补的信息来源：（i）在线体验，其中机器人使用它在部署期间收集的数据进行自我审计，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，机器人或人类审计员）进行干预以促使长期行为的改变。我们的项目将建立在不确定性下规划的最新进展，值得信赖的人工智能，稳健的规划和领域适应，因此有可能同时在多个领域推进最先进的技术。我们将展示模拟和真实世界的实验中，一个移动的机械手机器人必须导航大规模的，不熟悉的家庭和医院一样的建筑物，完成复杂的多阶段任务，包括定位关键的地方，与环境的互动，检索对象和人。我们打算从理论上证明和实证证明我们提出的方法的实用性，以快速，可靠地提高部署时间性能的各种服务机器人task.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Data-Efficient Policy Selection for Navigation in Partial Maps via Subgoal-Based Abstraction

通过基于子目标的抽象在部分地图中进行导航的数据高效策略选择

• DOI: 10.1109/iros55552.2023.10342047
• 发表时间: 2023
• 期刊: IEEE
• 作者: Paudel, Abhishek;Stein, Gregory J.
• 通讯作者: Stein, Gregory J.