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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.06557 |
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| _version_ | 1866909133769277440 |
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| author | De Lellis, Francesco Coraggio, Marco Foster, Nathan C. Villa, Riccardo Becchio, Cristina di Bernardo, Mario |
| author_facet | De Lellis, Francesco Coraggio, Marco Foster, Nathan C. Villa, Riccardo Becchio, Cristina di Bernardo, Mario |
| contents | We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator. We validate our approach on an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-driven architecture to encode information in the kinematics of robots and artificial avatars De Lellis, Francesco Coraggio, Marco Foster, Nathan C. Villa, Riccardo Becchio, Cristina di Bernardo, Mario Systems and Control Machine Learning Robotics We present a data-driven control architecture for modifying the kinematics of robots and artificial avatars to encode specific information such as the presence or not of an emotion in the movements of an avatar or robot driven by a human operator. We validate our approach on an experimental dataset obtained during the reach-to-grasp phase of a pick-and-place task. |
| title | Data-driven architecture to encode information in the kinematics of robots and artificial avatars |
| topic | Systems and Control Machine Learning Robotics |
| url | https://arxiv.org/abs/2403.06557 |