ハードウェアによる正則化：調整可能な受動コンプライアントリーダーアームで模倣学習のデータ一貫性を向上
Hardware-as-Regularization: Enhancing Data Consistency in Imitation Learning via an Adjustable Passive Compliant Leader Arm
低コスト受動リーダーアームの関節に機械式クラッチと螺旋ばねを組み込み、中立姿勢への復元力を物理的なL2正則化として働かせることで、模倣学習用データの関節軌道の一貫性と下流タスク成功率を改善した研究。
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著者: Hideyuki Ichiwara, Yushi Kaida, Hirokazu Ishida
分類: cs.RO
原文アブストラクト
Low-cost passive leader arms enable scalable data collection for imitation learning, but their lack of restoring forces leaves redundant joints unconstrained, yielding highly inconsistent joint-space trajectories. We address this with a low-cost passive joint module (an adjustable mechanical clutch and a 3D-printed spiral spring) that pulls each joint toward a user-defined neutral posture. This passive restoring force acts as a physical prior equivalent to joint-space $L_2$ regularization (``Hardware-as-Regularization''), improving kinematic consistency with shorter completion time than an unregularized arm and preserved movement smoothness. Experiments show substantial variance reduction over a naive passive arm, trajectory consistency on par with an active virtual spring of the same actuator class, and substantially higher downstream success rates (e.g., 73.3% vs. 36.7% on Block Stacking); a stiffness sweep shows consistency degrading monotonically as stiffness decreases, confirming that mechanical stiffness behaves like a regularization weight.