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.

翻译：我们提出了一种形态感知异构图神经网络（MI-HGNN），用于基于学习的接触感知。MI-HGNN的架构和连接性是根据机器人形态构建的，其中节点和边分别对应机器人的关节和连杆。通过将形态感知的约束融入神经网络，我们利用基于模型的知识改进了基于学习的方法。我们将所提出的MI-HGNN应用于两个接触感知问题，并使用两台四足机器人采集的真实世界和仿真数据进行了大量实验。我们的实验证明了该方法在有效性、泛化能力、模型效率和样本效率方面的优越性。我们的MI-HGNN仅使用0.21%的参数，就将一个利用机器人形态对称性的最先进模型的性能提升了8.4%。尽管本文中MI-HGNN应用于足式机器人的接触感知问题，但它可以无缝应用于其他类型的多体动力学系统，并有望改进其他机器人学习框架。我们的代码已在 https://github.com/lunarlab-gatech/Morphology-Informed-HGNN 公开。