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

职业：通过自动场景生成增强人机交互的鲁棒性

• 批准号: 2145077
• 负责人: Stefanos Nikolaidis
• 金额: $ 60万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145077&HistoricalAwards=false
• 关键词: CAREER; Enhancing; Robustness; Human; Robot

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.

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，还没有很好地理解如何构建机器人，以解决在测试此类系统时发现的不常见和不期望的行为。该学院早期职业发展（CAREER）奖支持基础研究，通过自动创建模拟的人机交互场景，揭示不良行为，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。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。该教育计划将与研究活动紧密结合，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，并激励他们追求科学，技术，工程和数学的职业生涯。该项目将通过自动生成和学习多样化，模拟中具有挑战性和现实性的场景。它将调查的质量多样性算法，有效地搜索场景空间的设计计算基础。然后，它将开发框架，将开发的算法与生成模型相结合，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的方法来形成学习课程，从而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。该项目得到了跨董事会机器人基础研究计划的支持，由工程局（ENG）和计算机与信息科学与工程局（CISE）共同管理和资助该奖项反映了NSF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。

项目成果

Covariance Matrix Adaptation MAP-Annealing

• DOI: 10.1145/3583131.3590389
• 发表时间: 2022-05
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: Matthew C. Fontaine;S. Nikolaidis

Deep Surrogate Assisted Generation of Environments

• DOI: 10.48550/arxiv.2206.04199
• 发表时间: 2022-06
• 期刊: ArXiv
• 作者: Varun Bhatt;Bryon Tjanaka;Matthew C. Fontaine;S. Nikolaidis

pyribs: A Bare-Bones Python Library for Quality Diversity Optimization

• DOI: 10.1145/3583131.3590374
• 发表时间: 2023-03
• 期刊: Proceedings of the Genetic and Evolutionary Computation Conference
• 作者: Bryon Tjanaka;Matthew C. Fontaine;David H. Lee;Yulun Zhang;Nivedit Reddy Balam;N. Dennler;Sujay S. Garlanka;Nikitas Dimitri Klapsis;S. Nikolaidis