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Our generated knowledge and methods have unlocked a myriad of robot-assisted learning applications, e.g., driving, playing tennis, and bicycling. This is understandable, as robotic rehabilitation can be considered the ultimate robot-human interaction paradigm; it entails a complex evolving co-learning relationship between robots and trainees. We build on our knowledge to explore novel applications, ranging from learning to control drones to bicycling.
Current Research Projects
Current Research Projects
Enhancing Motor Learning in Cycling Tasks
Riding a bicycle is a complex daily-life task that involves mastering balance and advanced techniques like cornering and steering while interacting in a highly unstable dynamic system. We evaluated the potential of using robotic assistance and optimal control, e.g., Model Predictive Control, to enhance this complex task.
Comparing Skill Acquisition in Virtual and Physical Environments
Despite the growing usage of immersive virtual environments for skill acquisition, questions remain about their efficacy relative to traditional methods. We investigate the differential impacts of motor skill acquisition compared to real-world settings as well as the difference in user experience.
Past Research Projects
Past Research Projects
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