Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914380315099136 |
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| author | Xie, Fengze Wei, Sizhe Song, Yue Yue, Yisong Gan, Lu |
| author_facet | Xie, Fengze Wei, Sizhe Song, Yue Yue, Yisong Gan, Lu |
| contents | We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning, that integrates robotic kinematic structures and morphological symmetries into a single graph network. These structural priors are embedded into the learning architecture as constraints, ensuring high generalizability, sample and model efficiency. The proposed MS-HGNN is a versatile and general architecture that is applicable to various multi-body dynamic systems and a wide range of dynamics learning problems. We formally prove the morphological-symmetry-equivariant property of our MS-HGNN and validate its effectiveness across multiple quadruped robot learning problems using both real-world and simulated data. Our code is made publicly available at https://github.com/lunarlab-gatech/MorphSym-HGNN/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_01297 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning Xie, Fengze Wei, Sizhe Song, Yue Yue, Yisong Gan, Lu Robotics Machine Learning We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning, that integrates robotic kinematic structures and morphological symmetries into a single graph network. These structural priors are embedded into the learning architecture as constraints, ensuring high generalizability, sample and model efficiency. The proposed MS-HGNN is a versatile and general architecture that is applicable to various multi-body dynamic systems and a wide range of dynamics learning problems. We formally prove the morphological-symmetry-equivariant property of our MS-HGNN and validate its effectiveness across multiple quadruped robot learning problems using both real-world and simulated data. Our code is made publicly available at https://github.com/lunarlab-gatech/MorphSym-HGNN/. |
| title | Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network for Robotic Dynamics Learning |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2412.01297 |