Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting
Human–robot interaction (HRI) is a key technology in the field of humanoid robotics, and motion imitation is one of the most direct ways to achieve efficient HRI. However, due to significant differences in structure, range of motion, and joint torques between the human body and robots, motion imitat...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Biomimetics |
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| Online Access: | https://www.mdpi.com/2313-7673/10/3/190 |
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| author | Xisheng Jiang Baolei Wu Simin Li Yongtong Zhu Guoxiang Liang Ye Yuan Qingdu Li Jianwei Zhang |
| author_facet | Xisheng Jiang Baolei Wu Simin Li Yongtong Zhu Guoxiang Liang Ye Yuan Qingdu Li Jianwei Zhang |
| author_sort | Xisheng Jiang |
| collection | DOAJ |
| description | Human–robot interaction (HRI) is a key technology in the field of humanoid robotics, and motion imitation is one of the most direct ways to achieve efficient HRI. However, due to significant differences in structure, range of motion, and joint torques between the human body and robots, motion imitation remains a challenging task. Traditional retargeting algorithms, while effective in mapping human motion to robots, typically either ensure similarity in arm configuration (joint space-based) or focus solely on tracking the end-effector position (Cartesian space-based). This creates a conflict between the liveliness and accuracy of robot motion. To address this issue, this paper proposes an improved retargeting algorithm that ensures both the similarity of the robot’s arm configuration to that of the human body and accurate end-effector position tracking. Additionally, a multi-person pose estimation algorithm is introduced, enabling real-time capture of multiple imitators’ movements using a single RGB-D camera. The captured motion data are used as input to the improved retargeting algorithm, enabling multi-robot collaboration tasks. Experimental results demonstrate that the proposed algorithm effectively ensures consistency in arm configuration and precise end-effector position tracking. Furthermore, the collaborative experiments validate the generalizability of the improved retargeting algorithm and the superior real-time performance of the multi-person pose estimation algorithm. |
| format | Article |
| id | doaj-art-b5ebada695094d04aa2c298f64a049e4 |
| institution | DOAJ |
| issn | 2313-7673 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj-art-b5ebada695094d04aa2c298f64a049e42025-08-20T02:42:39ZengMDPI AGBiomimetics2313-76732025-03-0110319010.3390/biomimetics10030190Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved RetargetingXisheng Jiang0Baolei Wu1Simin Li2Yongtong Zhu3Guoxiang Liang4Ye Yuan5Qingdu Li6Jianwei Zhang7School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaDepartment of Informatics, University of Hamburg, 20146 Hamburg, GermanyHuman–robot interaction (HRI) is a key technology in the field of humanoid robotics, and motion imitation is one of the most direct ways to achieve efficient HRI. However, due to significant differences in structure, range of motion, and joint torques between the human body and robots, motion imitation remains a challenging task. Traditional retargeting algorithms, while effective in mapping human motion to robots, typically either ensure similarity in arm configuration (joint space-based) or focus solely on tracking the end-effector position (Cartesian space-based). This creates a conflict between the liveliness and accuracy of robot motion. To address this issue, this paper proposes an improved retargeting algorithm that ensures both the similarity of the robot’s arm configuration to that of the human body and accurate end-effector position tracking. Additionally, a multi-person pose estimation algorithm is introduced, enabling real-time capture of multiple imitators’ movements using a single RGB-D camera. The captured motion data are used as input to the improved retargeting algorithm, enabling multi-robot collaboration tasks. Experimental results demonstrate that the proposed algorithm effectively ensures consistency in arm configuration and precise end-effector position tracking. Furthermore, the collaborative experiments validate the generalizability of the improved retargeting algorithm and the superior real-time performance of the multi-person pose estimation algorithm.https://www.mdpi.com/2313-7673/10/3/190improved retargetingmotion imitationmulti-person pose estimationhuman–robot interaction |
| spellingShingle | Xisheng Jiang Baolei Wu Simin Li Yongtong Zhu Guoxiang Liang Ye Yuan Qingdu Li Jianwei Zhang Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting Biomimetics improved retargeting motion imitation multi-person pose estimation human–robot interaction |
| title | Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting |
| title_full | Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting |
| title_fullStr | Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting |
| title_full_unstemmed | Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting |
| title_short | Multi-Humanoid Robot Arm Motion Imitation and Collaboration Based on Improved Retargeting |
| title_sort | multi humanoid robot arm motion imitation and collaboration based on improved retargeting |
| topic | improved retargeting motion imitation multi-person pose estimation human–robot interaction |
| url | https://www.mdpi.com/2313-7673/10/3/190 |
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