Effects of Operation Prediction Sharing for Collaborative Avatar Robot
A collaborative avatar robot, in which multiple users cooperate with a single-robot avatar, improves operability by leveraging the judgment and skills of both operators. However, collaboration among users is essential for a smooth operation. In this paper, we propose a system that provides a visual...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870150/ |
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| author | Hyuga Suzuki Hikari Yukawa Kouta Minamizawa Yoshihiro Tanaka |
| author_facet | Hyuga Suzuki Hikari Yukawa Kouta Minamizawa Yoshihiro Tanaka |
| author_sort | Hyuga Suzuki |
| collection | DOAJ |
| description | A collaborative avatar robot, in which multiple users cooperate with a single-robot avatar, improves operability by leveraging the judgment and skills of both operators. However, collaboration among users is essential for a smooth operation. In this paper, we propose a system that provides a visual display of operational predictions to facilitate mutual understanding of actions among users in a single robot arm as a collaborative avatar robot. The robotic arm was controlled by two operators, while the system predicts operations 0.3 seconds ahead using machine learning based on one operator’s actions. These predictions were visually presented to the other operator. Pick-and-place experiments were conducted, where two operators were assigned roles as leader and follower, with only the leader knowing the correct block to manipulate. The task performance and operability were evaluated with and without prediction system. Additionally, since operability and workability are affected by the control ratio of the two operators, three control ratio conditions were evaluated for comparison purposes. The results suggest that the followers’ subjective ratings in the sense of agency, smoothness, and workload significantly improved and that the operation effort of the leader significantly decreased while maintaining the sense of agency, reducing timing discrepancies between the operators. |
| format | Article |
| id | doaj-art-058fe371bc054910a924be05dc0c191b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-058fe371bc054910a924be05dc0c191b2025-02-12T00:01:45ZengIEEEIEEE Access2169-35362025-01-0113254192543110.1109/ACCESS.2025.353827710870150Effects of Operation Prediction Sharing for Collaborative Avatar RobotHyuga Suzuki0Hikari Yukawa1https://orcid.org/0009-0007-8013-1439Kouta Minamizawa2https://orcid.org/0000-0002-6303-5791Yoshihiro Tanaka3https://orcid.org/0000-0001-7917-1379Department of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, JapanDepartment of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, JapanGraduate School of Media Design, Keio University, Tokyo, JapanDepartment of Electrical and Mechanical Engineering, Graduate School of Engineering, Nagoya Institute of Technology, Nagoya, JapanA collaborative avatar robot, in which multiple users cooperate with a single-robot avatar, improves operability by leveraging the judgment and skills of both operators. However, collaboration among users is essential for a smooth operation. In this paper, we propose a system that provides a visual display of operational predictions to facilitate mutual understanding of actions among users in a single robot arm as a collaborative avatar robot. The robotic arm was controlled by two operators, while the system predicts operations 0.3 seconds ahead using machine learning based on one operator’s actions. These predictions were visually presented to the other operator. Pick-and-place experiments were conducted, where two operators were assigned roles as leader and follower, with only the leader knowing the correct block to manipulate. The task performance and operability were evaluated with and without prediction system. Additionally, since operability and workability are affected by the control ratio of the two operators, three control ratio conditions were evaluated for comparison purposes. The results suggest that the followers’ subjective ratings in the sense of agency, smoothness, and workload significantly improved and that the operation effort of the leader significantly decreased while maintaining the sense of agency, reducing timing discrepancies between the operators.https://ieeexplore.ieee.org/document/10870150/Body integrationembodied collaborationhuman-human cooperation/collaborationoperation predictionsensorimotor controlteleoperational robotics |
| spellingShingle | Hyuga Suzuki Hikari Yukawa Kouta Minamizawa Yoshihiro Tanaka Effects of Operation Prediction Sharing for Collaborative Avatar Robot IEEE Access Body integration embodied collaboration human-human cooperation/collaboration operation prediction sensorimotor control teleoperational robotics |
| title | Effects of Operation Prediction Sharing for Collaborative Avatar Robot |
| title_full | Effects of Operation Prediction Sharing for Collaborative Avatar Robot |
| title_fullStr | Effects of Operation Prediction Sharing for Collaborative Avatar Robot |
| title_full_unstemmed | Effects of Operation Prediction Sharing for Collaborative Avatar Robot |
| title_short | Effects of Operation Prediction Sharing for Collaborative Avatar Robot |
| title_sort | effects of operation prediction sharing for collaborative avatar robot |
| topic | Body integration embodied collaboration human-human cooperation/collaboration operation prediction sensorimotor control teleoperational robotics |
| url | https://ieeexplore.ieee.org/document/10870150/ |
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