Learning Environments with Augmentation and Robotics for Next-gen Emergency Responders


The “Learning Environments with Augmentation and Robotics for Next-gen Emergency Responders” (LEARNER) system is a collaborative effort between Virginia Tech, Texas A&M, University of Florida, SARCOS Robotics, and several other industry partners. The project focuses on developing a learning platform for Emergency Response (ER) personnel which combines Virtual Reality (VR) and exoskeleton technology to create an immersive simulated environment to practice ER situations. Our interdisciplinary team consists of researchers focused on VR integration, human systems integration, and adaptive learning techniques in safety-critical domains with industry professionals with established programs in robotic software and technology development. Finally, LEARNER is an adaptive accessible, modular learning platform whose architecture can be scaled to other industries.
The project goals are to 1) Build and Assess the LEARNER platform. LEARNER will support immersive skills acquisition and refinement with emerging HATs, and decision making in context-independent and authentic ER-based work scenarios. LEARNER has physical, augmented, and virtual reality components, where ER personnel will learn to work effectively with multiple HATs. 2) Design and iteratively assess Adaptive Learning Modules for cognitive and physical augmentation technologies in ER. We will develop, integrate, and assess EXO and AR learning modules into the LEARNER system. Our industry partner SARCOS Robotics will design an upper-body EXO emulator interface integrated into the core LEARNER system. 3) Facilitate Workforce Training with Emerging HATs in ER and Other Industries. We will produce a LEARNER system as well as a use-inspired socio-technical modeling framework that, by intent, scales to other industrial domains where human augmentation technologies can transform the national talent ecosystem.
Human augmentation technologies (HATs) such as robotics and augmented reality (AR), have the potential to dramatically transform the landscape of emergency response (ER) work, and improve the safety, performance, and quality of life of ER workers. Powered exoskeletons (EXOs), worn by workers, can augment physical capacity and hence potentially deliver machine-like power to workers, while still preserving human autonomy and decision making in unstructured and unpredictable environments. Human-machine interfaces, e.g., novel interfaces empowered via AR, can be used for the control and operation of ground robots, as well as wayfinding, increased situation awareness, and for improving team collaboration and decision making under stress. Hence, a context-sensitive and use-inspired combination of HATs is likely to tremendously impact both the ER work and worker. A vital bottleneck for this has been the lack of high-quality training for ER personnel that effectively integrates training on innovative HAT solutions during emergencies. Hence, new ER training paradigms are needed that are adaptive, affordable, accessible, and continually available for reskilling the ER workforce as technological capabilities continue to improve. To address this need, the LEARNER system serves to help our emergency responders, by building a more capable ER workforce, safeguarding their health, improving their career longevity, and ensuring our nation’s preparedness to prevent and respond to any emergencies.


Alexander Leonessa

Professor, Mechanical Engineering Department

Virginia Tech, Blacksburg, VA 24060

Divya Srinivasan

Professor, Industrial Engineering and Bioengineering

Clemson University, Clemson, SC 29634

Joseph Gabbard

Associate Professor, Industrial and Systems Engineering Department

Virginia Tech, Blacksburg, VA 24060

Jing Du

Associate Professor, Civil and Coastal Engineering Department

University of Florida, Gainesville, FL 32611

Ranjana Mehta
Associate Professor, Industrial& Systems Engineering Department

Texas A&M University, College Station, TX 77843