Robotics for Nursing: Opportunities and Challenges

Robotic technology has the potential to support nurses and caregivers in tasks, allowing them to spend more time on patient care and improving patient health outcomes. At the same time, the introduction of robots also brings forth several areas of concerns such as the increase in nursing workload due to required training and maintenance to use these complex systems.  The Workshop on Robotics for Nursing explores an emerging interdisciplinary research area focusing on the application of robotics technology for nursing and caregiving.

Sonia Chernova is an Associate Professor in the School of Interactive Computing at Georgia Tech. She directs the Robot Autonomy and Interactive Learning (RAIL), developing robots that are able to effectively operate in human environments. The lab’s research spans adjustable autonomy, semantic reasoning, human-robot interaction, and cloud robotics.  She also serves as the lead for the NSF AI Institute for Collaborative Assistance and Responsive Interaction for Networked Groups (AI-CARING), whose mission is to develop the next generation of personalized collaborative AI systems that improve the quality of life and independence of aging adults living at home.

NIRVANA: A Socially Assistive Robot and Virtual Reality System to Support Engagement in Long-Term Care for Older Adults with Mild Cognitive Impairment

This task will introduce NIRVANA (Non-Immersive Robot and Virtual Reality Activities in Aging), a novel system that integrates socially assistive robots with virtual reality to support dyadic interactions across social, cognitive, and physical domains. Developed through a user-centered design process, NIRVANA engages older adult pairs in cooperative virtual tasks while a humanoid robot (Nao) provides personalized encouragement and guidance. Staff input led to a simplified three-stage setup protocol, improving implementation in long-term care (LTC) despite initial learning curves. Real-world testing revealed common LTC challenges, such as staffing, space, and connectivity, underscoring the importance of seamless integration into care routines.

Human-AI-XR Collaboration in Nursing

The integration of Artificial Intelligence (AI) and Extended Reality (XR) is creating a powerful new paradigm for human-computer interaction, simulation, and intelligent system design. AI enhances XR by enabling adaptive environments, personalized feedback, and real-time decision support. In turn, XR supports AI development by offering synthetic data generation, improved model explainability, and rich interaction data. Together, AI and XR can significantly augment human performance in task-specific domains, such as nursing. This short talk will explore recent research at the intersection of AI, XR, and nursing, with a focus on gerontological nursing. Key topics include nurse training, emotion detection in older adults, fall risk and hazard identification, and collaborative interactions between nurses and assistive robotic systems.

Toward Autonomous Medical Robots Inside Living Tissue

Dr. Ron Alterovitz is the Lawrence Grossberg Distinguished Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. His research focuses on increasing the autonomy of robots by developing novel algorithms for robots to learn and plan their motions, with an emphasis on enabling robots to autonomously perform new, less invasive medical procedures and tasks in homes and workplaces. He leads the Computational Robotics Research Group, which addresses fundamental algorithmic challenges required to enable robots to safely and autonomously complete tasks in clinical and home environments.

Virtual Reality in Nursing Skill Assessment Training

In 2018, our institution launched the Methodist Proficiency Assessment and Competency (MPAC) certification to emphasize the importance of standardized physical assessments. This program increased the full assessment completion rates from 76% to 95%. Clinically, it improved early patient deterioration identification and reduced unplanned ICU admissions. Although it is a successful education intervention, the traditional simulation is labor-intensive and costly. To address these challenges, we explored virtual reality (VR) simulations. We developed two new physical assessment videos: a standard 2D video and a 360-degree VR video, both integrated into an online immersive learning platform (VIRTI). We found that VR offers a comparable and immersive learning experience, matching the high standards of our previous MPAC training.

Contextualizing Clinically Assistive Robots for Emergency Departments

As healthcare robots gain traction, HRI researchers are exploring the contextualization and implementation of privacy and safety guardrails. In this paper, we present two of our works exploring privacy and safety for healthcare robots. First, we discuss our online study exploring people's privacy perceptions of social healthcare robots. Second, we detail our ongoing research exploring the intersections of formal methods (control synthesis) and HRI towards correct-by-construction, safe robot systems for emergency departments (EDs). We discuss our collaboration with ED clinicians leading to the development of the Clinically Assistive Robot System for EDs (CARED), which we tested across two EDs. Finally, we outline future goals towards the development of privacy-aware, safe clinically assistive robots.

Home Robots as Care Partners: Opportunities and Challenges Illustrated with Examples

Human-scale home robots have the potential to benefit caregivers and care recipients, but challenges abound. In this talk, I will present illustrative examples of how future home robots might serve as care partners, including examples of people with severe mobility impairments using the Stretch mobile manipulator at home.