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

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.