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，人类机器人相互作用是通过人类主题实验测试的。尽管这些实验对于评估人类机器人相互作用是必要的，但它们通常在可以观察到的环境和人类行为的数量中受到限制。此外，尚不清楚如何构建测试此类系统时发现的不常见和不受欢迎的行为的机器人。这项教师早期职业发展（职业）奖通过自动创建模拟的人类机器人互动场景来支持基础研究，以改善人类机器人的互动，从而揭示出不可避免的行为，并将生成的场景整合到机器人的学习过程中。这项工作的结果将为机器人技术提供理论和实验工具，以允许机器人适应新的和具有挑战性的场景。教育计划与研究活动紧密整合，将介绍机器人教育和人工智能竞赛中的场景产生，以提高学生对机器人的能力和局限性的理解，并激发他们从事科学，技术，工程和数学的职业。该项目将通过自动产生和学习各种挑战，促进鲁棒的人类互动，从而促进科学，复杂的人类互动，以促进自动的人类互动，并实现各种各样的挑战，挑战，挑战。它将研究有效搜索场景空间的质量多样性算法设计的计算基础。然后，它将开发将开发算法与通用模型集成的框架，以优化复杂和现实的场景的低维空间。该项目将通过探索和表征有效选择挑战场景以形成学习课程的方法来结束场景生成和学习之间的循环。 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 precious of通过基金会的智力优点和更广泛的影响评估标准通过评估来支持。

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

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

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