Performance and Usability of Various Robotic Arm Control Modes from Human Force Signals

Abstract : Elaborating an efficient and usable mapping between input commands and output movements is still a key challenge for the design of robotic arm prostheses. In order to address this issue, we present and compare three different control modes, by assessing them in terms of performance as well as general usability. Using an isometric force transducer as the command device, these modes convert the force input signal into either a position or a velocity vector, whose magnitude is linearly or quadratically related to force input magnitude. With the robotic arm from the open source 3D-printed Poppy Humanoid platform simulating a mobile prosthesis, an experiment was carried out with eighteen able-bodied subjects performing a 3-D target-reaching task using each of the three modes. The subjects were given questionnaires to evaluate the quality of their experience with each mode, providing an assessment of their global usability in the context of the task. According to performance metrics and questionnaire results, velocity control modes were found to perform better than position control mode in terms of accuracy and quality of control as well as user satisfaction and comfort. Subjects also seemed to favor quadratic velocity control over linear (proportional) velocity control, even if these two modes did not clearly distinguish from one another when it comes to performance and usability assessment. These results highlight the need to take into account user experience as one of the key criteria for the design of control modes intended to operate limb prostheses.
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Article dans une revue
Frontiers in Neurorobotics, Frontiers, 2017, 11, 〈10.3389/fnbot.2017.00055〉
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Contributeur : Pierre-Yves Oudeyer <>
Soumis le : vendredi 1 décembre 2017 - 15:56:29
Dernière modification le : jeudi 11 janvier 2018 - 06:25:20


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Sébastien Mick, Daniel Cattaert, Florent Paclet, Pierre-Yves Oudeyer, Aymar De Rugy. Performance and Usability of Various Robotic Arm Control Modes from Human Force Signals. Frontiers in Neurorobotics, Frontiers, 2017, 11, 〈10.3389/fnbot.2017.00055〉. 〈hal-01653596〉



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