Intent-guided Adaptive Stiffening for Visuomotor Policies under Unknown Resistance

Manipulation tasks such as unscrewing a cap or sliding a part in contact with a surface require imposing force along a high-friction direction: the robot must overcome a resistance rather than yield to it. Generative visuomotor policies predict the motion for such tasks but not the authority needed to realize it, and that authority is bounded by the impedance controller beneath them: when the resistance exceeds what a compliant controller can exert under a bounded tracking error, the task is unattainable however accurate the prediction is. The required amount cannot be anticipated either, since it depends on a contact state the observation does not reveal. In this paper, we propose a residual stiffening layer placed underneath an unmodified policy. Resistance is recognized, without force sensing, from the mismatch between the motion the policy commands and the motion the robot realizes, and a four-mode supervisor raises the stiffness along a single direction read off the policy's own action chunk, retains it while the task progresses and releases it afterwards. The layer needs no compliance labels, no retraining and no re-collected demonstrations. On two real tasks, unscrewing a bottle cap and pushing a T-nut along a T-slot groove against non-homogeneous friction, evaluated at six resistance levels of which two were withheld from the demonstrations, the layer raises the success rate from 52% to 90% while producing 61% less peak off-axis force than a uniformly stiff controller at the same resistance.