## Summary of News: Human-Centered Robotics and AI Research by Assistant Professor Maria Kyrarini **News Title:** An engineering professor uses machine learning to improve human-centered robotics **Report Provider/Author:** Santa Clara University **Date/Time Period Covered:** Published on September 19, 2025. The content discusses ongoing and future research, implying a current and forward-looking timeframe. **Key News Identifiers:** * **URL:** `https://www.scu.edu/news-and-events/feature-stories/2025-feature-stories/stories/an-engineering-professor-uses-machine-learning-to-improve-human-centered-robotics.html` * **Topic:** Technology * **SubTopic:** Robot --- ### Main Findings and Conclusions Assistant Professor Maria Kyrarini at Santa Clara University is pioneering research in **human-centered robotics**, aiming to create robots that can **sense and adapt to users' emotional and physical states**, particularly fatigue. This research is supported by two major grants from the National Science Foundation (NSF). The core of her work involves using **machine learning and biosensors** to enable robots to detect when individuals, especially those with paralysis or mobility impairments, are experiencing fatigue. This allows the robots to proactively take on tasks or adjust their behavior to better assist the user. ### Key Statistics and Metrics * **Cognitive Fatigue Detection Accuracy (Speech-Only):** A Master's student has achieved an accuracy of **62%** in detecting cognitive fatigue using only speech. While acknowledged as "not great yet," the team is optimistic about improving this with more data. * **NSF Grant Funding:** Kyrarini has received **two major NSF grants**. The largest of these is a **$3 million grant** specifically for designing an AI-powered robotic-based manufacturing system. ### Significant Trends or Changes * **Evolution of Robotics:** Robotics has moved from novelties to everyday helpers, with a projected future where robots are commonplace in homes and workplaces. * **Personalized Robot Behavior:** The trend is shifting towards robots that can personalize their behavior, moving beyond simple programmability to adapt to individual user needs, emotional states, and physical conditions. * **Interdisciplinary Approach:** The "recyclofacturing" project highlights a growing trend in robotics research towards **interdisciplinary collaboration**, involving computer scientists, engineers, metal specialists, economists, sociologists, and industry stakeholders. ### Notable Risks or Concerns * **Early Stage of Technology:** Kyrarini acknowledges that the technology for detecting cognitive fatigue and emotional states is still in its **infancy**. * **Data Processing Needs:** Significant amounts of data need to be processed before extending research into recognizing emotions like sadness or frustration through speech and facial recognition. ### Material Financial Data * **$3 Million NSF Grant:** This substantial grant is dedicated to developing an AI-powered robotic manufacturing system focused on creating products from recycled metal, a project termed "recyclofacturing." ### Important Recommendations (Implied) * **Focus on User Needs:** The research strongly emphasizes the importance of understanding and prioritizing the holistic needs of the end-user, particularly for individuals with disabilities. * **Embrace Interdisciplinary Learning:** For students entering the robotics field, understanding diverse disciplines beyond engineering is crucial for successful robot design. * **Leverage AI and Machine Learning:** The continued development and application of AI and machine learning are vital for advancing the capabilities of responsive and human-centered robots. ### Contextual Interpretation of Numerical Data * **62% Accuracy:** This figure represents a preliminary success rate in a challenging area of AI research (detecting cognitive fatigue via speech). It indicates that the system is better than random chance but requires significant improvement to be reliable for practical applications. The context provided by Kyrarini ("it's not great yet") is crucial for understanding this metric. * **$3 Million Grant:** This is a significant financial investment from the NSF, underscoring the importance and potential impact of Kyrarini's work in sustainable manufacturing and AI-driven robotics. It signals a strong endorsement of the project's goals and feasibility. ### Key Projects and Research Areas * **Responsive Robots for People with Paralysis:** Designing robots that can interpret biological data (electrocardial and electrodermal activity) from wearable sensors to detect user fatigue and adjust robotic arm behavior. * **AI-Powered Robotic Manufacturing System:** A $3 million NSF grant is funding the creation of a system to build products from recycled metal, focusing on sustainability and advanced manufacturing. * **Cognitive Fatigue Detection:** Research exploring methods to detect cognitive fatigue through speech analysis, with ongoing efforts to improve accuracy. * **Future Extensions:** Potential research into recognizing emotions (sadness, frustration) via speech and facial recognition, and exploring the use of smartwatches instead of extensive biosensors. * **Multi-Arm Robotic Systems:** Investigating robotic systems that coordinate multiple arm bases for two-handed actions or multitasking. * **Predictive Robots:** Developing robots that can use a person's schedule to anticipate their needs for the day. ### Personal Background and Motivation Assistant Professor Maria Kyrarini's passion for robotics developed later in her career. Her early theoretical undergraduate studies in Greece contrasted with her hands-on experience during her Master's and Ph.D. at the University of Bremen, Germany. A pivotal experience was working on a project with a colleague who had multiple sclerosis, providing direct feedback that shaped her understanding of **co-designing** with end-users. This experience solidified her commitment to designing robots with the **holistic needs** of users in mind, particularly addressing challenges like cognitive fatigue for individuals relying on assistive technologies.
An engineering professor uses machine learning to improve human-centered robotics
Read original at News Source →What if a helper robot could sense when your brain was tired? Assistant professor Maria Kyrarini receives two major NSF grants to design responsive robots to assist people with paralysis and industrial workers.Robotics has come a long way in the last decade, going from rare novelties to everyday helpers doing everything from vacuuming homes to performing intricate surgeries.
And if you ask Assistant Professor Maria Kyrarini, this is just the tip of the iceberg. She believes that within a few years, robots will be in every person’s home and workplace. Unlike programmable robots, people are rarely predictable, and no two individuals have the same needs. So, given the increasing interdependence between humans and robots, Kyrarini explains that the bots of the future will need the ability to personalize their behavior based on their user’s emotional and physical state.
Using the power of machine learning and biosensors, Kyrarini’s cutting-edge research at Santa Clara University’s Robotic Systems Lab is helping robots detect when people with paralysis or other mobility impairments are fatigued, allowing the robots to take on more predictive tasks. It’s a sneak peek, she says, into the sci-fi promise of human-centered robotics.
Cura personalis, through robotics When Kyrarini reflects on when she knew engineering was right for her, she admits it was later in her career than most. Her undergraduate degree in her native Greece had been mostly theoretical due to the high costs of securing hardware in the classroom. But, during her Master’s at the University of Bremen in Germany, she finally got to dig into "real robots!
” That first encounter, so to speak, would shape the rest of her career. Staying in Bremen for her Ph.D., she was welcomed onto large projects developing robots that would work for and alongside humans. This included voice-controlled robotic systems for people who might be using their hands in manufacturing settings and, later, for people with disabilities.
“That was the most exciting project for me, because I had a colleague who had multiple sclerosis and was not able to move from the neck down,” Kyrarini says. “Whatever I developed, she would test and give me direct feedback. Having this co-designing process was really helpful.” Since then, she’s continued to design robots with the holistic needs of her end-user in mind, understanding that if a person with paralysis relies on voice commands to get robotic assistance, then cognitive fatigue might be a user’s greatest challenge.
Recently, Kyrarini, her students, and her partners at the University of Texas at Arlington have collaborated on a system that allows a mobile robotic arm to interpret biological data from wearable sensors that measure electrocardial and electrodermal activity to determine when the user is tired, and then adjust its behavior accordingly.
“We’re using machine learning to help these robots process these biological signals and figure out whether to ask the user more questions about their needs, or if they’re tired, take care of things for them and simply let the user hear and approve its plan for the day,” explains Kyrarini. Her partners at UT Arlington have recruited test users from the school’s two nationally-ranked basketball teams for wheelchair users; meanwhile, Kyrarini recruits non-STEM majors as an unbiased control group to compare against the UT Arlington results.
The next frontier While Kyrarini says this technology is still in its infancy, she’s still incredibly proud of the ways her team’s work has pushed the potential of this technology. “For example, I have a Master’s student who is trying to detect cognitive fatigue by only using speech. He’s only gotten an accuracy of 62%, so it’s not great yet, but we are hoping that if we get more data, he’ll get better results.
” In the future, they might extend the research into recognizing different emotions, like sadness or frustration, through speech and facial recognition, but there’s a lot of data their team has to process first. Other areas the team has explored include: 1) a robotic system that coordinates multiple arm bases, allowing for two-handed actions or multi-tasking, 2) replacing the extensive biosensors with a smart watch, or 3) a robot that can use a person’s schedule to predict what items they might need to get ready for the day.
Because the project relies on AI technology, Kyrarini is excited that Santa Clara not only gives undergrads access to hardware she didn’t have until grad school, but the school also offers a new Master’s degree in AI—a boon for students who want to be competitive applicants in the growing robotics industry.
More opportunities for hands-on learning are on the horizon, she adds. This year, Kyrarini received two NSF grants to use her cognitive fatigue research to improve the way we manufacture. The biggest is a $3 million grant to design an AI-powered robotic-based manufacturing system to create products from recycled metal.
While smarter, human-centered robots like Kyrarini’s would be an asset for any industry because of increased human safety and productivity, she’s particularly excited to be working in an industry connected to sustainability. Kyrarini and her School of Engineering colleague, Associate Professor Fatemeh Davoudi, will adjust similar robotic arms for manufacturing settings and ergonomic requirements.
From there, the scope of this work goes beyond just building a robot—at its core, the “recyclofacturing” project, as the team calls it, is about building a new way to build things. “We have so many interdisciplinary people on this project—computer scientists, engineers, metal specialists, economists, sociologists, and metal recycling stakeholders—so not only will this be very interesting, but I think it’s helpful that our engineering students will be exposed to so many other disciplines.
Robotics is the direction the world is moving, but to succeed in designing robots, you have to really understand the world first.”
