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陌生和不断变化的环境中行动。在人类操作员的要求下，这类机器人将被期望可靠地完成复杂的目标，尽管关于它们周围的信息丢失或过时：定位关键地点、运送补给和找到人员，即使它们不确定要找哪里。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，但学习驱动的策略很容易发生变化，导致在没有显著停机和监督的情况下在新的或不熟悉的环境中表现不佳，几乎没有改进的途径。糟糕的性能会招致不信任，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，该方法旨在使服务机器人在具有挑战性的、不熟悉的环境中具有最先进的性能，并在部署期间促进快速和可靠的改进。我们的贡献将使更多性能更好、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独有的能力，将有助于机器人培训的民主化，朝着更值得信赖和符合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，我们的研究计划与教育倡议相结合，吸引来自华盛顿地区高中的本科生和代表性不足群体的学生。我们的项目将开发一种原则性的方法来改进机器人在部署期间的行为，以在部分地图环境中进行长期规划，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-知情)和经典(STRIPS风格)规划的结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，允许完整性和内省，尽管缺少知识。我们的机器人将依赖两个互补的信息来源：(I)在线体验，其中机器人使用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到的行为；以及(Ii)专家指导，其中环境专家(例如，机器人或人类审计师)介入，以促使长期行为的改变。我们的项目将建立在不确定环境下的规划、值得信赖的人工智能、稳健的规划和领域适应方面的最新进展，因此有可能同时推进多个领域的最先进技术。我们将演示模拟和真实世界的实验，在这些实验中，移动机械手机器人必须在大规模、陌生的家庭和医院般的建筑中导航，以完成复杂的多阶段任务，包括定位关键位置、与环境交互以及取回对象和人员。我们打算从理论上证明并从经验上证明我们提出的方法的实用性，以快速可靠地改善各种服务机器人任务的部署时间性能。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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the request of human operators, such robots will be expected to reliably complete complex objectives despite missing or out-of-date information about their surroundings: locating key places, delivering supplies, and finding personnel, even when they are uncertain where to look.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.