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Passive Compliance Leading-Arm Proposed to Improve Data Consistency

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In an arXiv paper, Hideyuki Ichiwara proposes the Hardware-as-Regularization method, which uses low-cost passive joint modules (adjustable mechanical clutches plus 3D-printed helical springs) to pull each joint toward a user-defined neutral pose, using the passive restoring force as a physical prior for joint-space L2 regularization, in order to improve the trajectory consistency of imitation learning data. The paper's experiments claim that compared with a naive passive arm, this method significantly reduces variance, that its trajectory consistency is comparable to that of an active virtual spring at the same actuator level, and that downstream task success rates are markedly higher (e.g., 73.3% versus 36.7% for Block Stacking); a stiffness sweep shows that consistency decreases monotonically as stiffness decreases, indicating that mechanical stiffness behaves like a regularization weight.