Toward Understanding Key Estimation in Learning Robust Humanoid Locomotion
arxiv(2024)
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.
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