Accurate state estimation plays a critical role in ensuring the robust control of humanoid robots, particularly in the context of learning-based control policies for legged robots. However, there is a notable gap in analytical research concerning estimations. Therefore, we endeavor to further understand how various types of estimations influence the decision-making processes of policies. In this paper, we provide quantitative insight into the effectiveness of learned state estimations, employing saliency analysis to identify key estimation variables and optimize their combination for humanoid locomotion tasks. Evaluations assessing tracking precision and robustness are conducted on comparative groups of policies with varying estimation combinations in both simulated and real-world environments. Results validated that the proposed policy is capable of crossing the sim-to-real gap and demonstrating superior performance relative to alternative policy configurations.

翻译：精确的状态估计在确保类人机器人鲁棒控制中起着关键作用，尤其是在基于学习的足式机器人控制策略背景下。然而，关于估计的分析研究仍存在显著空白。因此，我们致力于进一步理解不同类型估计对策略决策过程的影响。本文通过显著度分析识别关键估计变量并优化其组合，为类人运动任务中学习型状态估计的有效性提供了定量见解。在仿真和真实环境中，我们对具有不同估计组合的策略对照组进行了跟踪精度和鲁棒性评估。结果验证了所提策略能够跨越仿真到现实的鸿沟，并展现出优于其他策略配置的卓越性能。