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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/2402.18675 |
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| _version_ | 1866910348287672320 |
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| author | Jiang, Shuo Zhang, Jinkun Wong, Lawson |
| author_facet | Jiang, Shuo Zhang, Jinkun Wong, Lawson |
| contents | For a robot, its body structure is an a-prior knowledge when it is designed. However, when such information is not available, can a robot recognize it by itself? In this paper, we aim to grant a robot such ability to learn its body structure from exteroception and proprioception data collected from on-body sensors. By a novel machine learning method, the robot can learn a binary Heterogeneous Dependency Matrix from its sensor readings. We showed such matrix is equivalent to a Heterogeneous out-tree structure which can uniquely represent the robot body topology. We explored the properties of such matrix and the out-tree, and proposed a remedy to fix them when they are contaminated by partial observability or data noise. We ran our algorithm on 6 different robots with different body structures in simulation and 1 real robot. Our algorithm correctly recognized their body structures with only on-body sensor readings but no topology prior knowledge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18675 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Robot Body Schema Learning from Full-body Extero/Proprioception Sensors Jiang, Shuo Zhang, Jinkun Wong, Lawson Robotics For a robot, its body structure is an a-prior knowledge when it is designed. However, when such information is not available, can a robot recognize it by itself? In this paper, we aim to grant a robot such ability to learn its body structure from exteroception and proprioception data collected from on-body sensors. By a novel machine learning method, the robot can learn a binary Heterogeneous Dependency Matrix from its sensor readings. We showed such matrix is equivalent to a Heterogeneous out-tree structure which can uniquely represent the robot body topology. We explored the properties of such matrix and the out-tree, and proposed a remedy to fix them when they are contaminated by partial observability or data noise. We ran our algorithm on 6 different robots with different body structures in simulation and 1 real robot. Our algorithm correctly recognized their body structures with only on-body sensor readings but no topology prior knowledge. |
| title | Robot Body Schema Learning from Full-body Extero/Proprioception Sensors |
| topic | Robotics |
| url | https://arxiv.org/abs/2402.18675 |