MIT creates neural blueprint enabling soft robots to learn once and adapt on the fly

MIT and Singapore researchers developed an AI control system that lets soft robotic arms learn a wide repertoire of motions once, then adjust to new scenarios without retraining. The approach combines task learning, rapid adaptation, and stability guarantees in a single framework. The breakthrough brings soft robotics closer to human-like adaptability for assistive, rehabilitation, and medical applications. Unlike rigid robots, soft robots move using flexible materials but have been notoriously difficult to control due to unpredictable shape changes.

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