CAREER: Enhancing the Robustness of Human-Robot Interactions via Automatic Scenario Generation

我们需要新的技术来评估人类和机器人在家庭和工作场所的互动。传统上，人机交互是通过人体实验来测试的。虽然这些实验对于评估人机交互是必要的，但它们通常受限于可以观察到的环境和人类行为的数量。此外，人们还不太清楚如何构建机器人来解释在测试此类系统时发现的不常见和不良行为。该学院早期职业发展（Career）奖支持通过自动创建揭示不良行为的模拟人机交互场景以及将生成的场景集成到机器人的学习过程中来改善人机交互的基础研究。这项工作的结果将为机器人领域提供理论和实验工具，使机器人能够适应新的和具有挑战性的场景。与研究活动紧密结合，教育计划将在机器人教育和人工智能竞赛中引入场景生成，以提高学生对机器人能力和局限性的理解，并激励他们从事科学、技术、工程和数学方面的职业。该项目将通过在模拟中自动生成和学习不同的、具有挑战性的和现实的场景，推进鲁棒的、复杂的人机交互科学。它将研究有效搜索场景空间的高质量多样性算法设计的计算基础。然后，它将开发框架，将开发的算法与生成模型集成，以优化复杂和现实场景的低维空间。该项目将通过探索和描述有效选择具有挑战性的场景以形成学习课程的方法来关闭场景生成和学习之间的循环。通过开源软件和研讨会传播所有已开发的算法，将有助于将质量多样性优化和场景生成的想法带给更广泛的机器人受众。该项目由跨部门机器人基础研究项目支持，由工程（ENG）和计算机与信息科学与工程（CISE）联合管理和资助。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。

There is a need for new techniques to assess humans and robots’ interactions at home and in the workplace. Traditionally, human-robot interaction is tested with human subject experiments. While these experiments are necessary to evaluating human-robot interactions, they are often limited in the number of environments and human behaviors that can be observed. Furthermore, it is not well understood how to build robots that account for infrequent and undesirable behaviors found when testing such systems. This Faculty Early Career Development (CAREER) award supports fundamental research to improve human-robot interactions by automatically creating simulated human-robot interaction scenarios that reveal undesirable behaviors, as well as integrating the generated scenarios into the robot’s learning process. Results from this work will provide the field of robotics with a theoretical and experimental tools for allowing the robot to adjust to new and challenging scenarios. Tightly integrated with the research activities, the education plan will introduce scenario generation in robotics education and artificial intelligence competitions to improve students' understanding of robots' capabilities and limitations and inspire them to pursue a career in science, technology, engineering, and mathematics.This project will advance the science of robust, complex human-robot interaction by automatically generating and learning from diverse, challenging and realistic scenarios in simulation. It will investigate computational foundations for the design of quality diversity algorithms that efficiently search the scenario space. It will then develop frameworks that integrate the developed algorithms with generative models to optimize a low-dimensional space of complex and realistic scenarios. The project will close the loop between scenario generation and learning by exploring and characterizing methods for efficiently selecting challenging scenarios to form a curriculum for learning. Dissemination of all developed algorithms through open-source software and workshops will help bring ideas from quality diversity optimization and scenario generation to a wider robotics audience.This project is supported by the cross-directorate Foundational Research in Robotics program, jointly managed and funded by the Directorates for Engineering (ENG) and Computer and Information Science and Engineering (CISE).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.