Teaching robots to move naturally usually forces a tough choice: flexibility or safety. Not anymore. Meet S²-NNDS: a new framework that uses neural networks to teach robots fluid, expressive motion while baking in mathematical guarantees for stability. No robotic jitter.
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Replacing rigid, old-school models with neural nets paired with "split conformal prediction", this tech predicts potential errors and wraps the robot’s motion in Lyapunov safety certificates. Flexible movement with a mathematical promise that the robot won't crash or drift.
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Tested on everything from 2D handwriting to real robotic arms in cluttered spaces, it’s a game changer. It even cleans up "bad" human demos, learning safe, stable paths from sloppy inputs. It handles complex, obstacle-heavy environments where other methods fail.
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Struggling with ED? Ro Sparks get you harder, faster, if prescribed. Compounded, not FDA approved. See safety info link on image.
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