Interaction-Stiffness-Guided Basis Allocation in Dynamic Movement Primitives for Efficient Skill Transfer
This study addresses the limitation of predefined basis function layouts in Dynamic Movement Primitives (DMPs), which struggle to accommodate varying precision requirements across different trajectory phases. To overcome this, we propose SC-DMPs, a method that constructs a criticality index by integrating operator-robot interaction stiffness with trajectory consistency. Through inverse cumulative criticality mapping, SC-DMPs dynamically reallocates the centers and bandwidths of basis functions within the canonical phase domain. Experimental evaluations on handwriting and real-world robotic tasks demonstrate that SC-DMPs significantly enhances trajectory reproduction, endpoint generalization, and precision during critical phases while preserving model compactness and stability. This work establishes a novel paradigm for adaptive, high-precision skill learning.