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Impact-aware compliant foot placement for quiet humanoid locomotion via reinforcement learning

Jul 2026 · Robot Learning · pp. 1-24 · 0 citations · 29 references

TL;DR

A control method for reducing foot–ground contact noise during humanoid walking, achieving compliant contact and continuous regulation of locomotion noise by establishing a foot corner contact model along with a virtual compliance parameter is proposed.

Abstract

: Recent advances in legged robots have substantially improved their locomotion capabilities over outdoor and complex terrains. However, quiet locomotion for humanoid robots in noise-sensitive indoor environments remains underexplored, despite its growing importance in human-centered applications. While encouraging progress has been made in quadrupedal robots, transferring the quiet locomotion ability to humanoid robots remains nontrivial due to their fundamentally different foot-ground contact patterns. This paper proposes a control method for reducing foot–ground contact noise during humanoid walking, achieving compliant contact and continuous regulation of locomotion noise by establishing a foot corner contact model along with a virtual compliance parameter. The experimental results show that the average sound pressure level is reduced by 4.88 dB.

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