Collaborative robot dynamics with physical human–robot interaction and parameter identification with PINN

Xingyu Yang, Zhengxue Zhou, Leihui Li, Xuping Zhang*

*Corresponding author for this work

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Collaborative robots are increasingly being used in dynamic and semi-structured environments because of their ability to perform physical Human–Robot Interaction (pHRI) to ensure safety. Therefore, it is crucial to model the dynamics of collaborative robots during pHRI to gain valuable insights into the system’s behavior when in contact with humans. In this work, a generic dynamic model is proposed for the quasi-static contact phase of pHRI, which considers the interaction dynamics and the complete structural dynamics of the collaborative robot. Moreover, a hybrid physics-informed neural network (PINN) is proposed, which utilizes a recurrent neural network (RNN) and the Runge–Kutta method to identify the joint dynamic parameters without complicated regressor construction. Experiments are conducted using a UR3e collaborative robot, and the PINN is trained using the acquired data. The results demonstrate the effectiveness of the PINN in identifying joint dynamics without prior knowledge, and the dynamic simulation of pHRI is consistent with the experimental results. The proposed model and PINN-based identification approach have the potential to improve safety and productivity in industrial environments by facilitating the control of pHRI.
Original languageEnglish
Article number105439
JournalMechanism and Machine Theory
Publication statusPublished - Nov 2023


  • Physical human–robot interaction
  • Collaborative robot
  • Dynamic modeling
  • Joint flexibility
  • Parameter identification
  • Physics-informed neural network


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