Adaptive Physical Human–Robot Interaction Via a Passivity-Aware Model Predictive Variable Admittance Control

Abstract

Physical Human–Robot Interaction (pHRI) requires control frameworks that balance accuracy, compliance, and safety under variable human behaviors. This paper proposes a novel Model Predictive Variable Admittance (MPVA) framework that integrates trajectory tracking, interaction force directionality, and passivity constraints into an online real-time optimization scheme. The proposed architecture is implemented on a 7-DoF Kinova Jaco-2 robot and validated experimentally through mixed assistive and resistive modes with multiple subjects performing pHRI tasks. Results supported by both objective metrics and subjective evaluation through a NASA TLX survey show that the MPVA achieves competitive tracking accuracy, reducing physical effort with minimal passivity violations compared to other algorithmic baselines such as fixed-gain admittance and fuzzy-based adaptive admittance. This demonstrates safe and effective human-robot physical interaction across diverse modes.