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A "copilot" framework using a motion-state-aware critic can significantly improve the fidelity of EEG-decoded hand trajectories for robotic control, even when using EEG data alone.
Achieve a 40% jump in success rates on real-world contact-rich manipulation by intelligently scheduling force feedback into visual-motor policies.
