Learning Quiet Walking for a Small Home Robot
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Date
2025
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
As home robotics gains traction, robots are in creasingly 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.
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published
Editor
Book title
2025 IEEE International Conference on Robotics and Automation (ICRA)
Journal / series
Volume
Pages / Article No.
15285 - 15291
Publisher
IEEE
Event
42nd IEEE International Conference on Robotics and Automation (ICRA 2025)
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Software
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Date created
Subject
Organisational unit
09570 - Hutter, Marco / Hutter, Marco
Notes
Conference lecture held on May 22, 2025
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Related publications and datasets
Is new version of: https://doi.org/10.48550/arXiv.2502.10983