About 12% of U.S. adults have difficulty with mobility, including walking or climbing stairs. Current technologies for helping people manage these issues are often expensive, heavy, and hard to use. The goal of this research is to develop a new paradigm for assistive robotics that will make it possible to take such systems from the laboratory to widely accessible tools for people. The research team will combine advances in robotic exoskeletons for human joints, simulation enabled by artificial intelligence, and other approaches to design lightweight, wearable robotic systems that can be personally controlled. The investigators will also test the systems in different settings to improve their usability. The project will develop new science and technology that have the potential to help people to perform daily activities, along with their quality of life and overall well-being. The project will design and test new assistive robotic systems by integrating artificial intelligence, robotics, biosensors, rehabilitation medicine, gerontology, and neurorehabilitation. The research will use a modular design approach, that can adapt to individual needs and daily activities without extensive calibration. By leveraging computational modeling and physics-informed deep reinforcement learning, the systems will learn control strategies from computer simulations and user feedback to deliver personalized support that addresses an individual’s mobility challenges and needs. The objective is