Physics-Informed Passive Motion Paradigm for Parallel Robots: A High-Precision Motor-Primitives Framework

Abstract

Complex embodied systems, whether biological or robotic, must continuously generate goal-directed behaviors while preserving coherence between motor intention and physical feasibility. In parallel robots, this link between intention and mechanics becomes particularly challenging due to their nonlinear, over-constrained kinematics and the absence of intuitive motor primitives. This letter introduces a passive motion paradigm for parallel robots using self-supervised physics-informed neural networks, which reformulates motion generation as the dynamic unfolding of motor primitives driven by attractor fields in actuator space. Unlike traditional forward or optimization-based formulations, the framework integrates analytical kinematics with neural fields to ensure both physical consistency and adaptive motion generation. The method estimates the Jacobian matrix as a physically constrained neural field, merging analytical structure with data-driven learning to achieve robust and interpretable behavior without relying on iterative numerical solvers. Theoretical analysis, simulations, and physical experiments demonstrate the framework’s accuracy, stability, and adaptability across different parallel mechanisms.