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Generative AI that imitates human motion

Walking and running is notoriously difficult to recreate in robots. Now, a group of researchers has overcome some of these challenges by creating an innovative method that employs central pattern generators -- neural circuits located in the spinal cord that generate rhythmic patterns of muscle activity -- with deep reinforcement learning. The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from walking to running, and allows for adapting to environments with unstable surfaces.

Robotic system feeds people with severe mobility limitations

Cornell researchers have developed a robotic feeding system that uses computer vision, machine learning and multimodal sensing to safely feed people with severe mobility limitations, including those with spinal cord injuries, cerebral palsy and multiple sclerosis.

New approach uses generative AI to imitate human motion

An international group of researchers has created a new approach to imitating human motion by combining central pattern generators (CPGs) and deep reinforcement learning (DRL). The method not only imitates walking and running motions but also generates movements for frequencies where motion data is absent, enables smooth transition movements from walking to running, and allows for adaptation to environments with unstable surfaces.
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