Impedance Variation and Learning Strategies in Human-Robot Interaction

• Author(s) / Creator(s)

  • Mojtaba Sharifia, Amir Zakerimanesh, Javad K. Mehr, Ali Torabi, Vivian K. Mushahwar, Mahdi Tavakoli

• In this survey, various concepts and methodologies developed over the past two decades for varying and learning the impedance or admittance of robotic systems that physically interact with humans are explored. For this purpose, the assumptions and mathematical formulations for online adjustment of impedance models and controllers for physical humanrobot interaction (HRI) are categorized and compared. In this systematic review, studies on (a) variation and (b) learning of appropriate impedance elements are taken into account. These strategies are classified and described in terms of their objectives, points of view (approaches), signal requirements (including position, HRI force and EMG activity). Different
  methods involving linear/nonlinear analyses (e.g., optimal control design and nonlinear Lyapunov-based stability guarantee) and Gaussian approximation algorithms (e.g., GMM-based and DMPbased strategies) are reviewed. Current challenges and research trends in physical HRI are finally discussed.

• Date created

  2021-01-01

• Subjects / Keywords

  • Human-robot interaction (HRI); impedance and admittance models; impedance variation; impedance learning; impedance control; robot learning; robot stability.

• Type of Item

  Review

• DOI

  https://doi.org/10.7939/r3-md7z-xe80

• License

  Attribution-NonCommercial 4.0 International