CAREER: Uncovering Structure in Human-Robot Systems for Trajectory Prediction and Crowd Navigation

职业：揭示用于轨迹预测和人群导航的人机系统结构

• 批准号: 2143435
• 负责人: Katherine Driggs-Campbell
• 金额: $ 50.02万
• 依托单位: University of Illinois at Urbana-Champaign
• 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: 2022-02-15 至 2027-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143435&HistoricalAwards=false
• 关键词: CAREER; Uncovering; Structure; Human; Robot

Intelligent robots and autonomous systems are quickly becoming commonplace in our daily lives. However, the desirable impacts of autonomy are only achievable if the underlying algorithms can handle the unique challenges that humans present. To design safe, trustworthy autonomous systems, there is a need to transform how intelligent systems interact, influence, and predict human agents. This Faculty Early Career Development (CAREER) project will focus on the understanding how humans and mobile robots can and should interact. First, predicting human trajectories in crowded spaces through structured representations will be considered. Second, robot strategies for using these predictions for intelligent decision-making will be considered. The aim is to balance efficiency and safety, guaranteeing reliable performance even in the presence of erratic human behavior and sensor uncertainty. The approaches will be evaluated on real-world robots, motivated by high-impact problem domains, including agricultural robots (which is currently facing a labor crisis, resulting in an increased demand for robotics); collaborative manufacturing (which is seeing a rise in popularity of co-robots); and transportation (where behavior prediction and interaction remains one of key challenges for autonomy). This project will support robotics education through the development of robotics coursework in PrairieLearn, an online problem-driven learning system developed at University of Illinois Urbana-Champaign (UIUC), and K-12 outreach to spur interest in STEM and robotics. On-line tutorials and short courses will also aid in both integration of research with education and transition to industry.Decades of robotics and automation research is reaching a critical turning point: the gap between theory and full-scale deployment is beginning to close. However, the desirable impacts of autonomy are only achievable if the underlying algorithms can successfully consider human behavior. To design trustworthy systems, there is a need to transform how intelligent systems interact, influence, and predict humans. This project will examine the interaction between humans and mobile robots, aiming to uncover the underlying structure within this interaction and enable fluid robot navigation. Trajectory prediction and crowd navigation will be examined. First, interaction graphs will be introduced as a formal representation that captures the coupling between agents and allows for tractability and computational efficiency through factorizations. This framework allows modeling types of interactions that capture variable and dynamic relationships between agents. Second, this representation and prediction insight will be combined into robust navigation, providing safety even in the presence of uncertainty. Improving mobile robot interaction will impact many different application sectors, including agricultural robots, collaborative manufacturing, and transportation. This project will support robotics education through the development of robotics coursework in PrairieLearn, an online problem-driven learning system developed at UIUC, and tutorials for industry partners interested in learning about research in this area. 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.

智能机器人和自主系统正在我们的日常生活中变得越来越普遍。然而，只有当底层算法能够处理人类提出的独特挑战时，自治的理想影响才能实现。为了设计安全、可信的自治系统，需要改变智能系统如何交互、影响和预测人类代理。这个教师早期职业发展（CAREER）项目将侧重于了解人类和移动的机器人如何能够和应该互动。首先，将考虑通过结构化表示来预测拥挤空间中的人类轨迹。其次，将考虑使用这些预测进行智能决策的机器人策略。其目的是平衡效率和安全性，即使在人类行为不稳定和传感器不确定的情况下也能保证可靠的性能。这些方法将在现实世界的机器人上进行评估，这些机器人受到高影响力问题领域的激励，包括农业机器人（目前正面临劳动力危机，导致对机器人的需求增加）;协作制造（协作机器人的普及率上升）;以及运输（行为预测和交互仍然是自主性的关键挑战之一）。该项目将通过开发PrairieLearn中的机器人课程来支持机器人教育，PrairieLearn是伊利诺伊大学香槟分校（UIUC）开发的在线问题驱动学习系统，K-12推广活动旨在激发对STEM和机器人的兴趣。 在线教程和短期课程也将有助于研究与教育的整合以及向工业的过渡。数十年的机器人和自动化研究正达到一个关键的转折点：理论与全面部署之间的差距正开始缩小。然而，只有在底层算法能够成功地考虑人类行为的情况下，自治的理想影响才能实现。为了设计出值得信赖的系统，需要改变智能系统如何与人类互动、影响和预测人类。该项目将研究人类和移动的机器人之间的交互，旨在揭示这种交互的底层结构，并实现流体机器人导航。轨迹预测和人群导航将被检查。首先，交互图将被引入作为一个正式的表示，捕捉代理之间的耦合，并允许通过因式分解的易处理性和计算效率。该框架允许对捕获代理之间的变量和动态关系的交互类型进行建模。其次，这种表示和预测洞察力将结合到强大的导航中，即使在存在不确定性的情况下也能提供安全性。改善移动的机器人交互将影响许多不同的应用领域，包括农业机器人、协同制造和运输。 该项目将通过在UIUC开发的在线问题驱动学习系统PrairieLearn中开发机器人课程来支持机器人教育，并为有兴趣了解该领域研究的行业合作伙伴提供教程。该项目由跨部门的机器人基础研究项目支持，该项目由工程部（ENG）和计算机与信息科学与工程部（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智力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

Occlusion-Aware Crowd Navigation Using People as Sensors

使用人作为传感器的遮挡感知人群导航

• DOI: 10.1109/icra48891.2023.10160715
• 发表时间: 2023
• 期刊: IEEE International Conference on Robotics and Automation (ICRA
• 作者: Mun, Ye-Ji;Itkina, Masha;Liu, Shuijing;Driggs-Campbell, Katherine
• 通讯作者: Driggs-Campbell, Katherine

Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory Prediction

• DOI: 10.1109/icra46639.2022.9811632
• 发表时间: 2022-02
• 期刊: 2022 International Conference on Robotics and Automation (ICRA)
• 作者: Aamir Hasan;Pranav Sriram;K. Driggs-Campbell

Combining Model-Based Controllers and Generative Adversarial Imitation Learning for Traffic Simulation

结合基于模型的控制器和生成对抗性模仿学习进行交通仿真

• DOI: 10.1109/itsc55140.2022.9922261
• 发表时间: 2022
• 期刊: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC
• 作者: Chen, Haonan;Ji, Tianchen;Liu, Shuijing;Driggs-Campbell, Katherine
• 通讯作者: Driggs-Campbell, Katherine

Robust Output Feedback MPC with Reduced Conservatism under Ellipsoidal Uncertainty

• DOI: 10.1109/cdc51059.2022.9992704
• 发表时间: 2020-08
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: Tianchen Ji;Junyi Geng;Katherine Driggs Campbell

Structural Attention-Based Recurrent Variational Autoencoder for Highway Vehicle Anomaly Detection

用于公路车辆异常检测的基于结构注意的循环变分自编码器

• 发表时间: 2023
• 期刊: Autonomous agents and multiagent systems
• 作者: Chakraborty, Neeloy;Hasan, Aamir;Liu, Shuijing;Ji, Tianchen;Liang, Weihang;McPherson, D Livingston;Driggs-Campbell, Katherine
• 通讯作者: Driggs-Campbell, Katherine