対称性を考慮した異種グラフニューラルネットワークによるテンセグリティロボットの端部-地面接触推定
Tensegrity Robot Endcap-Ground Contact Estimation with Symmetry-aware Heterogeneous Graph Neural Network
テンセグリティロボットの接触状態を、専用センサーを使わずIMUとケーブル長の履歴から推定する対称性考慮の異種グラフニューラルネットワークを提案し、接触支援InEKFと統合して姿勢推定精度を向上させた。
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著者: Wenzhe Tong, Yicheng Jiang, Chi Zhang, Maani Ghaffari, Xiaonan Huang
分類: cs.RO
原文アブストラクト
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
