As home robotics gains traction, robots are increasingly integrated into households, offering companionship and assistance. Quadruped robots, particularly those resembling dogs, have emerged as popular alternatives for traditional pets. However, user feedback highlights concerns about the noise these robots generate during walking at home, particularly the loud footstep sound. To address this issue, we propose a sim-to-real based reinforcement learning (RL) approach to minimize the foot contact velocity highly related to the footstep sound. Our framework incorporates three key elements: learning varying PD gains to actively dampen and stiffen each joint, utilizing foot contact sensors, and employing curriculum learning to gradually enforce penalties on foot contact velocity. Experiments demonstrate that our learned policy achieves superior quietness compared to a RL baseline and the carefully handcrafted Sony commercial controllers. Furthermore, the trade-off between robustness and quietness is shown. This research contributes to developing quieter and more user-friendly robotic companions in home environments.

翻译：随着家用机器人逐渐普及，机器人越来越多地融入家庭生活，提供陪伴与协助。四足机器人，尤其是外形类似犬类的机器人，已成为传统宠物的热门替代品。然而，用户反馈指出这些机器人在家庭环境中行走时产生的噪音问题，特别是响亮的脚步声。为解决这一问题，我们提出了一种基于仿真到现实的强化学习方法，以最小化与脚步声高度相关的足部接触速度。我们的框架包含三个关键要素：学习可变的PD增益以主动阻尼和强化每个关节、利用足部接触传感器，以及采用课程学习逐步对足部接触速度施加惩罚。实验表明，与强化学习基线以及精心设计的索尼商业控制器相比，我们学习到的策略实现了更优的静音效果。此外，研究还展示了鲁棒性与静音性之间的权衡关系。这项研究有助于开发更安静、更人性化的家用机器人伴侣。