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User evaluation of an interactive learning framework for single-arm and dual-arm robots

AutorJevtić, Aleksandar; Colomé, Adrià ; Torras, Carme
Fecha de publicación2016
EditorSpringer Nature
CitaciónSocial Robotics: 52-61 (2016)
SerieLecture Notes in Computer Science 9979
ResumenSocial robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett¿s 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.
DescripciónTrabajo presentado a la International Conference on Social Robotics (ICSR), celebrada en Kansas (USA) del 1 al 3 de noviembre de 2016.
Versión del editorhttps://doi.org/10.1007/978-3-319-47437-3_6
URIhttp://hdl.handle.net/10261/168123
Identificadoresdoi: 10.1007/978-3-319-47437-3_6
isbn: 978-3-319-47437-3
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