Quadruped robots are designed to achieve agile and robust locomotion by drawing inspiration from legged animals. However, most existing control methods for quadruped robots lack a key capacity observed in animals: the ability to exhibit diverse compliance behaviors while ensuring stability when experiencing external forces. In particular, achieving adjustable compliance while maintaining robust safety under force disturbances remains a significant challenge. In this work, we propose a safety aware compliant locomotion framework that integrates adjustable disturbance compliance with robust failure prevention. We first train a force compliant policy with adjustable compliance levels using a teacher student reinforcement learning framework, allowing deployment without explicit force sensing. To handle disturbances beyond the limits of compliant control, we develop a safety oriented policy for rapid recovery and stabilization. Finally, we introduce a learned safety critic that monitors the robot's safety in real time and coordinates between compliant locomotion and recovery behaviors. Together, this framework enables quadruped robots to achieve smooth force compliance and robust safety under a wide range of external force disturbances.

翻译：四足机器人通过借鉴有腿动物的运动原理，旨在实现敏捷且鲁棒的步态运动。然而，现有的大多数四足机器人控制方法缺乏动物所具备的一项关键能力：在承受外力时能够表现出多样化的柔顺行为，同时确保稳定性。具体而言，如何在力扰动下实现可调节的柔顺性并保持鲁棒的安全性，仍然是一个重大挑战。本研究提出了一种安全感知的柔顺步态控制框架，该框架将可调节的扰动柔顺性与鲁棒的故障预防机制相结合。我们首先利用师生强化学习框架训练了一个具有可调柔顺级别的力柔顺策略，从而无需显式力传感即可部署。为处理超出柔顺控制极限的扰动，我们开发了一种面向安全的策略，用于实现快速恢复与稳定。最后，我们引入了一个学习得到的安全评判器，用于实时监测机器人的安全状态，并协调柔顺步态与恢复行为。该框架共同使四足机器人能够在多种外部力扰动下实现平滑的力柔顺与鲁棒的安全性。