用机器人结构信息增强图神经网络，提升足式机器人接触感知性能
MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception
- 基于机器人结构构建图网络，节点为关节，边为连杆
- 在真实与仿真数据上，性能比顶尖模型高8.4%，参数仅需0.21%
- 适用于多体系统，可推广至其他机器人学习框架
我们提出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。该网络的架构与连接关系由机器人形态决定，节点代表关节，边代表连杆。通过将形态约束融入神经网络，结合模型驱动知识，提升了学习方法的效果。我们在两种接触感知任务中应用了MI-HGNN，使用两只四足机器人采集的真实与仿真数据进行了大量实验。结果表明，该方法在有效性、泛化能力、模型效率和样本效率方面均表现优异。相较于利用机器人对称性的先进模型，MI-HGNN性能提升8.4%，且仅需其0.21%的参数量。尽管本工作聚焦于足式机器人的接触感知，但该方法可无缝应用于其他多体动力系统，并有望改进其他机器人学习框架。代码已公开：https://github.com/lunarlab-gatech/Morphology-Informed-HGNN。
原文摘要 · Abstract (English)
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.
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