迈向对鲁棒人形机器人运动学习中关键估计的理解
Toward Understanding Key Estimation in Learning Robust Humanoid Locomotion
AI总结：
该研究针对人形机器人运动学习中状态估计分析不足的问题，通过显著性分析识别关键估计变量并优化组合，经仿真与真实环境验证，所提策略可跨越sim-to-real差距且性能更优。
摘要：
准确的状态估计在确保人形机器人鲁棒控制方面起着关键作用，尤其是在基于学习的足式机器人控制策略的场景中。然而，关于估计的分析研究存在显著空白。因此，我们致力于进一步理解不同类型的估计如何影响策略的决策过程。本文中，我们针对学习得到的状态估计的有效性提供了量化见解，采用显著性分析来识别人形运动任务的关键估计变量并优化其组合。我们在仿真和真实环境中，对具有不同估计组合的策略对照组开展了跟踪精度和鲁棒性评估。结果证实，所提出的策略能够跨越仿真到现实的差距，且相较于其他策略配置表现出更优性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
