将四足机器人本体感知数据转为图像，提升接触状态预测精度
Proprioceptive Image: An Image Representation of Proprioceptive Data from Quadruped Robots for Contact Estimation Learning
- 将关节位置、IMU等信号转化为带结构的二维图像
- 在真实与仿真环境中准确率提升至94.5%，窗口缩短15倍
- 适合做机器人步态学习与地形适应性控制的研究者
本文提出一种新方法，将四足机器人本体感知的时序数据转换为具有结构的二维图像，使卷积神经网络可用于学习运动相关任务。该方法融合多个本体感知信号（如关节位置、惯性测量单元读数、足部速度）的时序动态，同时保留机器人形态结构的空间分布。这种转换捕捉了信号间相关性与步态依赖模式，提供了比直接时序处理更丰富的特征空间。研究将其应用于接触状态估计问题，这是实现复杂地形稳定自适应运动的关键能力。在真实数据集和模拟环境中的实验表明，基于图像的表示在预测精度和泛化能力上均优于传统序列模型。相比最近提出的MI-HGNN方法，本方法在接触数据集上将准确率从87.7%提升至94.5%，且使用15倍短的窗口长度。
原文摘要 · Abstract (English)
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.
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