MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception
| Autor: | Butterfield, Daniel, Garimella, Sandilya Sai, Cheng, Nai-Jen, Gan, Lu |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | We present a Morphology-Informed Heterogeneous Graph Neural Network (MI-HGNN) for learning-based contact perception. The architecture and connectivity of the MI-HGNN are constructed from the robot morphology, in which nodes and edges are robot joints and links, respectively. By incorporating the morphology-informed constraints into a neural network, we improve a learning-based approach using model-based knowledge. We apply the proposed MI-HGNN to two contact perception problems, and conduct extensive experiments using both real-world and simulated data collected using two quadruped robots. Our experiments demonstrate the superiority of our method in terms of effectiveness, generalization ability, model efficiency, and sample efficiency. Our MI-HGNN improved the performance of a state-of-the-art model that leverages robot morphological symmetry by 8.4% with only 0.21% of its parameters. Although MI-HGNN is applied to contact perception problems for legged robots in this work, it can be seamlessly applied to other types of multi-body dynamical systems and has the potential to improve other robot learning frameworks. Our code is made publicly available at https://github.com/lunarlab-gatech/Morphology-Informed-HGNN. Comment: 6 pages, 5 figures; This work has been submitted to the IEEE for possible publication |
| Databáze: | arXiv |
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