Abstract
Social 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.
This work was supported by the Beatriu de Pinós fellowship, reference num.: 2013 BP-B 00239, jointly funded by the Government of Catalunya, Spain and the European Commission FP7 COFUND programme. The work was partially supported by the EU CHIST-ERA I-DRESS project, reference num. PCIN-2015-147, and the national Spanish project RobInstruct, reference num. TIN2014-58178-R.
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Notes
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Additional material at: http://www.iri.upc.edu/groups/perception/adapt2.
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Jevtić, A., Colomé, A., Alenyà, G., Torras, C. (2016). User Evaluation of an Interactive Learning Framework for Single-Arm and Dual-Arm Robots. In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_6
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