MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception
<|reference_start|>MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception: 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.<|reference_end|>
@article{butterfield2024mi-hgnn:,
    title={MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for
  Legged Robot Contact Perception},
    author={Daniel Butterfield, Sandilya Sai Garimella, Nai-Jen Cheng, Lu Gan},
    journal={arXiv preprint arXiv:2409.11146},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.11146},
    primaryClass={cs.RO}
}