Impedance Variation and Learning Strategies in Human-Robot Interaction
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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.
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http://purl.org/coar/resource_type/c_efa0
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en