固有感覚イメージ：四足歩行ロボットの接触推定学習のための固有感覚データの画像表現
Proprioceptive Image: An Image Representation of Proprioceptive Data from Quadruped Robots for Contact Estimation Learning
四足歩行ロボットの固有感覚時系列データを、ロボットの形態構造を保った2次元画像に変換し、CNNで接触推定を学習する手法を提案。従来法より短い窓で高精度を達成。
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著者: Gabriel Fischer Abati, João Carlos Virgolino Soares, Giulio Turrisi, Victor Barasuol, Claudio Semini
分類: cs.RO
原文アブストラクト
This paper presents a novel approach for representing proprioceptive time-series data from quadruped robots as structured two-dimensional images, enabling the use of convolutional neural networks for learning locomotion-related tasks. The proposed method encodes temporal dynamics from multiple proprioceptive signals, such as joint positions, IMU readings, and foot velocities, while preserving the robot's morphological structure in the spatial arrangement of the image. This transformation captures inter-signal correlations and gait-dependent patterns, providing a richer feature space than direct time-series processing. We apply this concept in the problem of contact estimation, a key capability for stable and adaptive locomotion on diverse terrains. Experimental evaluations on both real-world datasets and simulated environments show that our image-based representation consistently enhances prediction accuracy and generalization over conventional sequence-based models, underscoring the potential of cross-modal encoding strategies for robotic state learning. Our method achieves superior performance on the contact dataset, improving contact state accuracy from 87.7% to 94.5% over the recently proposed MI-HGNN method, using a 15 times shorter window size.
