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A deterministic 25 Hz tactile reflex controller is used as a collection-time teacher that shapes grasping behavior in demonstrations for policy learning, while disturbance rejection remains controller-dependent, revealing the boundary of tactile-free policy.
Treating action conditioning as a structured process rather than a global compression could redefine how we model high-dimensional dexterous actions in AI.
Hierarchical control in humanoid robots can solve complex leg-critical tasks, but performance is fragile and heavily reliant on the choice of motion tracker.
