Durham, North Carolina, USA: Tania Roy, a researcher at Duke University's Pratt School of Engineering, is part of a team led by Yiran Chen that is developing next-generation hardware for AI-powered robots. The project focuses on building neuromorphic processors inspired by the human brain and nervous system to improve the speed and energy efficiency of robotic systems.
According to Duke University, the new hardware is expected to be up to ten times faster and one hundred times more energy efficient than existing technologies. Researchers believe current processing delays from cameras and sensors remain a major challenge in expanding AI-powered robotics beyond specialized applications. The project also includes experts from Georgia Tech and Brookhaven National Laboratory.
Chen said the completed system is envisioned as a synthetic organism with neuromorphic computing components functioning as its muscles, skeleton and nervous system.
In a separate initiative, Gaurav Arya is leading a project that applies artificial intelligence to improve DNA origami, a technique used to design nanoscale materials for energy-related applications.
The research team aims to develop AI-based computational models that simplify the process of folding DNA into highly precise structures. The project includes collaborations with Stefan Zauscher from Duke University, along with researchers from Georgia Tech, Emory University and Lawrence Berkeley National Laboratory.
Arya said the research is expected to unlock new possibilities in biomaterials, with potential applications in energy production, chemical manufacturing and quantum computing.
The Genesis Mission, launched by the U.S. Department of Energy, is designed to accelerate scientific discovery by combining artificial intelligence, supercomputing, quantum technologies and advanced scientific instruments. The initiative brings together researchers from government agencies, academia, industry and philanthropic organizations.
Apart from the engineering projects involving Roy and Arya, Duke University will also lead two additional Trinity College of Arts & Sciences research initiatives. One project, led by Steffen A. Bass, will use AI to improve atomic nucleus models by linking experimental data with heavy-ion collision simulations. Another, led by Kate Scholberg, will develop AI tools for rapid analysis of neutrino data, enabling astronomers to respond more quickly to stellar events such as supernovas.
