Deep Imitation Learning of Nonlinear Model Predictive Control Laws for Safe Physical Human-Robot Interaction

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1 Citation (Scopus)

Abstract

This paper proposes motion planning algorithms for industrial manipulators in the presence of human operators based on deep neural networks (DNNs), aimed at imitating the behavior of a nonlinear model predictive control (NMPC) scheme. The proposed DNN solutions retain the safety features of NMPC in terms of speed and separation monitoring, defined according to the guidelines in the ISO/TS 15066 standard. At the same time, they improve the robot performance in terms of task completion time, and of a-posteriori evaluation of the NMPC cost function on experimental data. The reasons for this improvement are the reduced computational delay of running a DNN compared to solving the nonlinear programs associated to NMPC, and the ability to implicitly learn how to predict the human operator's motion from the training set.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Collision avoidance
  • Industrial robotics
  • Kinematics
  • Manipulators
  • model predictive control
  • Neural networks
  • neural networks
  • physical human-robot interaction
  • Robots
  • Safety
  • Service robots

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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