Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans. Existing studies either develop conservative controllers (< 1.0 m/s) to ensure safety, or focus on agility without considering potentially fatal collisions. This paper introduces Agile But Safe (ABS), a learning-based control framework that enables agile and collision-free locomotion for quadrupedal robots. ABS involves an agile policy to execute agile motor skills amidst obstacles and a recovery policy to prevent failures, collaboratively achieving high-speed and collision-free navigation. The policy switch in ABS is governed by a learned control-theoretic reach-avoid value network, which also guides the recovery policy as an objective function, thereby safeguarding the robot in a closed loop. The training process involves the learning of the agile policy, the reach-avoid value network, the recovery policy, and an exteroception representation network, all in simulation. These trained modules can be directly deployed in the real world with onboard sensing and computation, leading to high-speed and collision-free navigation in confined indoor and outdoor spaces with both static and dynamic obstacles.

翻译：腿式机器人在杂乱环境中导航时，需兼具敏捷性以高效完成任务，同时确保安全以避免与障碍物或人类发生碰撞。现有研究要么采用保守控制器（<1.0米/秒）来保障安全性，要么专注于敏捷性而忽略潜在致命碰撞。本文提出“敏捷而安全”（Agile But Safe, ABS）——一种基于学习的控制框架，使四足机器人能够实现敏捷且无碰撞的运动。ABS包含一个敏捷策略，用于在障碍物间执行敏捷运动技能，以及一个恢复策略以预防故障，两者协同实现高速无碰撞导航。ABS中的策略切换由一个基于学习的控制理论启发可达-避免值网络控制，该网络同时作为目标函数引导恢复策略，从而在闭环中保障机器人安全。训练过程涉及敏捷策略、可达-避免值网络、恢复策略及外部感知表征网络的学习，均在仿真环境中完成。这些训练模块可直接部署于具备机载传感与计算能力的真实世界，在包含静态和动态障碍物的室内外狭小空间中实现高速无碰撞导航。