Passive leader arm improves robot imitation learning consistency
Hardware-as-Regularization: Enhancing Data Consistency in Imitation Learning via an Adjustable Passive Compliant Leader Arm
Robotics
Summary
Collecting robot movement data by copying human actions can be tricky because simple mechanical arms don’t naturally guide joints back to a steady position, causing inconsistent movements. The authors created a low-cost upgrade that gently pulls each joint toward a neutral pose, like a spring. This helps the robot produce more reliable and smoother motions and finish tasks faster without complicated electronics. The new setup’s mechanical stiffness acts like a form of data regularizing, improving the quality of learned robot behaviors.
What this means in practice
- •For robotics engineers: Enhance data collection setups for robot imitation learning tasks by adding passive joint modules that improve movement consistency and smoothness.
- •For robotic system integrators: Use low-cost mechanical spring components to improve physical demonstration rigs that train robot manipulators for assembly and stacking tasks.
Authors
Hideyuki Ichiwara, Yushi Kaida, Hirokazu Ishida
Abstract
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.