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Data-Inferred Personalized Human-Robot Models for Iterative Collaborative Output Tracking

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Abstract

This article studies collaborative human- robot output tracking when the desired output is only known to the human but not to the robot controller. The main contribution of this article is to propose and establish convergence conditions for an iterative learning algorithm that updates the robot input using (i) the effect of the human action on the combined human-robot output tracking (which includes the effect of the human-response dynamics) and (ii) data-inferred human-robot models. This allows the iterative learning control (ILC) to be personalized for each individual human operator. Additionally, experimental results are presented to illustrate the iterative learning approach. Results show that, with the proposed approach, the robot can learn to collaboratively track the output with 10.0% error, which is close to twice the robot noise of 4.6% of the desired output. Furthermore, the data-inferred models provided evidence of the effect of the human operator’s dynamics on the co-tracking task.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA

    Jonathan Realmuto, Rahul B. Warrier & Santosh Devasia

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  1. Jonathan Realmuto
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  2. Rahul B. Warrier
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  3. Santosh Devasia
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Correspondence to Jonathan Realmuto.

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Realmuto, J., Warrier, R.B. & Devasia, S. Data-Inferred Personalized Human-Robot Models for Iterative Collaborative Output Tracking. J Intell Robot Syst 91, 137–153 (2018). https://doi.org/10.1007/s10846-017-0653-z

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  • DOI: https://doi.org/10.1007/s10846-017-0653-z

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