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Researchers have developed a deep reinforcement learning-based controller for lower limb rehabilitation exoskeletons, which aims to assist patients with neuromuscular disorders in walking autonomously and robustly. The controller uses a decoupled simulation training technique with three independent networks to learn optimal control policies for the exoskeleton and the human, minimizing interaction forces and maintaining balance and stability.
