Paper Info
Title
User Evaluation of an Interactive Learning Framework for Single-Arm and Dual-Arm Robots.
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.
Year
DOI
Venue
2016
10.1007/978-3-319-47437-3_6
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
Robot manipulators,User intervention,Robot adaptation,Gesture recognition,Visual servoing,Reinforcement learning
Robot learning,Social robot,Computer vision,Robot control,Robotic arm,Computer science,Personal robot,Gesture recognition,Artificial intelligence,Robot,Mobile robot
Conference
Volume
ISSN
Citations 
9979
0302-9743
1
PageRank 
References 
Authors
0.36
10
4
Name
Order
Citations
PageRank
Aleksandar Jevtic18210.40
Adria Colome2305.89
Guillem Alenyà321927.43
Carme Torras41155115.66