In this paper, we propose a method that applies Inertial-Leg (IL) tokenization to an attention-based network for proprioceptive state estimation in legged robots. Unlike existing learning-based state estimators that concatenate all sensor measurements into a single flat vector, the proposed architecture represents inertial measurements and leg-wise measurements as individual tokens and uses an attention mechanism to learn the relative importance of each measurement.This design allows the network to reweight each measurement according to the current contact condition, reflecting the fact that the reliability of forward kinematic measurements depends on whether the corresponding foot is in contact. Unlike conventional contact-aided estimators, however, the proposed method learns this behavior without relying on an explicit contact estimator or on explicit measurement updates based on a stationary contact assumption. To validate the proposed method, we conducted experiments on a Unitree Go1 robot, including debris terrain not modeled in simulation and gait patterns not seen during training. Experimental results show that the proposed method achieves better estimation performance than existing learning-based state estimators under unseen gait patterns and also improves performance over contact-aided model-based methods.

翻译：本文提出了一种方法，将惯性-腿部令牌化应用于基于注意力的网络，以实现腿式机器人的本体状态估计。与现有的基于学习的状态估计器将所有传感器测量值拼接成单一扁平向量的做法不同，本文架构将惯性测量和腿部测量分别表示为独立令牌，并利用注意力机制学习每个测量值的相对重要性。这种设计使网络能够根据当前接触条件重新调整每个测量值的权重，反映了前向运动学测量的可靠性取决于相应脚部是否接触地面的事实。然而，与传统的接触辅助估计器不同，所提方法无需依赖显式的接触估计器或在静止接触假设下进行显式测量更新，即可学习此类行为。为验证所提方法，我们在宇树Go1机器人上进行了实验，包括仿真中未建模的碎石地形以及训练中未出现的步态模式。实验结果表明，所提方法在未见步态模式下相比现有基于学习的状态估计器实现了更优的估计性能，同时相较基于接触辅助的模型方法也提升了性能。