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ForceDelta-VLA is presented, a correction-distillation framework that constructs an explicit force-correction target using paired predictions from a frozen teacher's force-conditioned and learned force-agnostic modes, and reduces mean peak contact force over successful trials by approximately 26% on both platforms.
Force-aware evaluation metrics reveal hidden performance gaps in humanoid robot control that conventional benchmarks miss.
