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

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

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.