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APT achieves substantial improvements in instruction generalization for VLA models by effectively decoupling language and action learning, addressing a critical data imbalance issue.
A novel GRU-based dynamics model for tendon-driven robots eliminates self-excited oscillations, achieving superior robustness and accuracy in control.
Achieving a 50.9% reduction in tracking error, this framework transforms how we control complex tendon-driven robots.
