QuietPaw：通过多功能噪声偏好对齐学习四足机器人运动
QuietPaw: Learning Quadrupedal Locomotion with Versatile Noise Preference Alignment
AI总结：
针对四足机器人运动性能与脚步噪声约束的平衡问题，提出QuietPaw框架及其条件噪声约束策略（CNCP），通过价值表示分解实现单一策略跨噪声水平泛化，在仿真和现实中均实现了连续可调的降噪与性能平衡。
摘要：
当四足机器人在全功率下运行时，会产生巨大的脚步噪声，这在家庭、办公室和医院等以人为中心的环境中可能会造成干扰。因此，平衡运动性能与噪声约束对于四足机器人在现实世界中的成功部署至关重要。然而，实现自适应噪声控制具有挑战性，原因在于（a）敏捷性与噪声最小化之间的权衡，（b）需要在多种部署条件下进行泛化，以及（c）难以根据噪声需求有效地调整策略。我们提出了QuietPaw，一个包含我们提出的条件噪声约束策略（CNCP）的框架，这是一种基于约束学习的算法，通过将策略行为以降噪水平为条件来实现灵活的、具有噪声感知的运动。我们在评论者中利用价值表示分解，将状态表示与依赖于条件的表示解耦，这使得单一的多功能策略能够在无需重新训练的情况下跨噪声水平进行泛化，同时改善敏捷性与降噪之间的帕累托权衡。我们在仿真和现实世界中验证了我们的方法，证明CNCP能够有效平衡运动性能和噪声约束，实现连续可调的降噪。
英文摘要：
When operating at their full capacity, quadrupedal robots can produce loud footstep noise, which can be disruptive in human-centered environments like homes, offices, and hospitals. As a result, balancing locomotion performance with noise constraints is crucial for the successful real-world deployment of quadrupedal robots. However, achieving adaptive noise control is challenging due to (a) the trade-off between agility and noise minimization, (b) the need for generalization across diverse deployment conditions, and (c) the difficulty of effectively adjusting policies based on noise requirements. We propose QuietPaw, a framework incorporating our Conditional Noise-Constrained Policy (CNCP), a constrained learning-based algorithm that enables flexible, noise-aware locomotion by conditioning policy behavior on noise-reduction levels. We leverage value representation decomposition in the critics, disentangling state representations from condition-dependent representations and this allows a single versatile policy to generalize across noise levels without retraining while improving the Pareto trade-off between agility and noise reduction. We validate our approach in simulation and the real world, demonstrating that CNCP can effectively balance locomotion performance and noise constraints, achieving continuously adjustable noise reduction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
