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

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，这种机器人将被期望可靠地完成复杂的目标，尽管缺少或过时的周围环境信息：定位关键地点，运送物资，寻找人员，即使他们不确定去哪里找。虽然机器学习已被证明是该领域良好行为的重要组成部分，但学习驱动的策略可能很容易改变，导致在新的或不熟悉的环境中表现不佳，而且在没有大量停机和监督的情况下几乎没有改进的资源。表现不佳会引发不信任，从而限制了服务型机器人的采用，从而限制了它们在从家庭到医院等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，该方法旨在使服务机器人在具有挑战性的陌生环境中表现出最先进的性能，并在部署期间促进快速可靠的改进。我们的贡献将使性能更强、更可靠、更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业用户快速纠正机器人的行为，这是我们在该领域提出的方法所独有的能力，这将有助于机器人训练的民主化，朝着更值得信赖和道德的机器人迈出一步。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，我们的研究项目与教育计划相结合，吸引本科生和来自华盛顿地区高中代表性不足群体的学生。我们的项目将开发一种原则性方法，用于改善机器人在部分映射环境中长期规划部署期间的行为，强调可靠性、数据效率和性能。我们将证明，由我们的抽象提供的数据驱动（学习信息）和经典（带状）规划的耦合将是这一进步的关键促成因素；学习将增强基于模型的计划，允许完整性和自省，尽管缺少知识。我们的机器人将依赖于两个互补的信息来源：(i)在线体验，机器人使用在部署期间收集的数据进行自我审核，并重新培训和调整其学习行为；（ii）专家指导，由环境专家（例如机器人或人类审计员）干预，以促使长期行为发生变化。我们的项目将建立在不确定性、可信赖的人工智能、稳健的规划和领域适应方面的最新进展的基础上，因此有可能同时在多个领域推进最先进的技术。我们将演示模拟和现实世界的实验，其中移动机械手机器人必须在大型，不熟悉的家庭和医院式建筑物中导航，以完成复杂的多阶段任务，包括定位关键位置，与环境相互作用以及检索物体和人员。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，以快速可靠地提高各种服务机器人任务的部署时性能。该奖项反映了美国国家科学基金会的法定使命，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。

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the request of human operators, such robots will be expected to reliably complete complex objectives despite missing or out-of-date information about their surroundings: locating key places, delivering supplies, and finding personnel, even when they are uncertain where to look.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.