Collaborative Research: CyberTraining: Implementation: Medium: Cybertraining for Expanding the Use of Digital Twin Technologies in Robotics

About This Grant

Robotic systems that must operate in hazardous or remote environments, such as future extraterrestrial exploration vehicles, autonomous construction equipment, and search-and-rescue robots, cannot be designed safely and efficiently by trial and error. This project creates open instructional materials and trains thousands of learners in physics-based "Digital Twin" technologies, enabling engineers, scientists, and students to design and test robots through computer simulation before hardware is built. By lowering cost and risk, simulation accelerates innovation and discovery, strengthens U.S. leadership in advanced manufacturing and space technology, and expands economic opportunity. A key feature of the project is its anyone-anywhere-anytime approach to learning and training, that hones the modeling and simulation skills of participants ranging from California State University students to experts at NASA, Department of Energy, and other government agencies. The project's long-term impact is a workforce fluent in robotics simulation, Digital Twin technologies, and high-performance computing that gains hands-on experience with the open-source Chrono simulator, positioning these professionals to advance national prosperity and public welfare. This project executes a CyberTraining plan focused on enabling the use of Digital Twins in Robotics through two coordinated activities: (i) creating instructional material, and (ii) delivering a CyberTraining (CT) program. For (i), this project provides the opportunity to carry out an instructional-material tokenization process and flipping of three Chrono-enabled or Chrono-related classes; prepare Jupyter notebooks for self-paced training; and produce new widely available Chrono models and documentation that encourage CI tool use through smoother onboarding. For (ii), this team will facilitate CyberTraining via both synchronous and asynchronous instructional approaches. These "create content and execute training" activities will anchor a technical effort that has five CyberTraining components. The first CT component expands participation in computational science through a collaboration between project investigators from the University of Wisconsin–Madison (UW–Madison) and the California State University (CSU) System. Every 12 months, a cohort of students from four CSU campuses — Los Angeles, Northridge, Fullerton, and Pomona — will be trained in using simulation in lunar exploration robotics. These activities include a visit to NASA's Jet Propulsion Laboratory for a firsthand look at rover operations; online training on simulation use in robotics; and a two-week Summer School at UW–Madison that concludes with a robotics team competition. The second CT component will train practitioners in the use of Chrono via tutorials held in conjunction with an annual consortium meeting that fosters outreach to industry. The project also includes revamping three UW–Madison courses that underpin the concept of Digital Twins in robotics. The fourth CT component provides asynchronous training to thousands of anonymous Chrono users. This training draws on materials uploaded to the NSF's ACCESS ecosystem and the Project Chrono website and is further supported by a large language model expert agent piloted by this group. Lastly, the project engages students participating in NASA's Lunar Autonomy Challenge. Together, these activities create a scalable, reproducible pipeline that couples flipped-classroom pedagogy, cloud-ready software containers, and software-in-the-loop testbeds. Expected outcomes of this project include a curated library of Digital Twin models, freely available training artifacts hosted on the NSF's ACCESS ecosystem, and a measurable increase in CI-savvy robotics researchers capable of executing large-scale simulation studies on high-performance computers. 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.