用对称图网络从自感数据推断张拉整体机器人接触状态
Tensegrity Robot Endcap-Ground Contact Estimation with Symmetry-aware Heterogeneous Graph Neural Network
- 基于二面体对称性设计异构图网络，提升样本效率
- 仅用20%数据即达15%更高精度和5%更高F1分数
- 适合无接触传感器的柔性机器人状态估计场景
张拉整体机器人具有轻量化和抗扰结构，但因柔性和分布式地面接触导致状态估计困难。本文提出一种对称感知异构图神经网络（Sym-HGNN），仅利用惯性测量单元（IMU）和缆绳长度历史等本体感知数据，直接推断接触状态，无需专用接触传感器。网络在消息传递过程中融入机器人的二面体对称性 $D_3$，提升样本效率与泛化能力。预测的接触状态被整合进先进的接触辅助不变扩展卡尔曼滤波器（InEKF），实现更优姿态估计。仿真结果表明，该方法在仅使用20%训练数据的情况下，相比CNN和MI-HGNN基线，精度最高提升15%，F1分数提升5%，且保持低漂移、物理一致的状态估计效果，与真实接触结果相当。该工作展示了全本体感知在张拉整体机器人中实现高精度、鲁棒状态估计的潜力。代码已开源：https://github.com/Jonathan-Twz/Tensegrity-Sym-HGNN。
原文摘要 · Abstract (English)
Tensegrity robots possess lightweight and resilient structures but present significant challenges for state estimation due to compliant and distributed ground contacts. This paper introduces a symmetry-aware heterogeneous graph neural network (Sym-HGNN) that infers contact states directly from proprioceptive measurements, including IMU and cable-length histories, without dedicated contact sensors. The network incorporates the robot's dihedral symmetry $D_3$ into the message-passing process to enhance sample efficiency and generalization. The predicted contacts are integrated into a state-of-the-art contact-aided invariant extended Kalman filter (InEKF) for improved pose estimation. Simulation results demonstrate that the proposed method achieves up to 15% higher accuracy and 5% higher F1-score using only 20% of the training data compared to the CNN and MI-HGNN baselines, while maintaining low-drift and physically consistent state estimation results comparable to ground truth contacts. This work highlights the potential of fully proprioceptive sensing for accurate and robust state estimation in tensegrity robots. Code available at: https://github.com/Jonathan-Twz/Tensegrity-Sym-HGNN
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