敏捷且安全：学习无碰撞的高速足式运动
Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
AI总结：
针对足式机器人导航中敏捷性与安全性难以兼顾的问题，提出基于学习的ABS控制框架，通过敏捷策略、恢复策略及可达-规避值网络协同，实现四足机器人在杂乱环境中的高速无碰撞运动，且可直接部署到现实场景。
摘要：
在杂乱环境中导航的足式机器人必须同时具备敏捷性以高效执行任务，以及安全性以避免与障碍物或人类发生碰撞。现有研究要么开发保守的控制器（速度低于1.0 m/s）以确保安全，要么仅关注敏捷性而不考虑可能致命的碰撞。本文提出了Agile But Safe（ABS，敏捷且安全），这是一种基于学习的控制框架，可实现四足机器人的敏捷且无碰撞运动。ABS包含一个用于在障碍物间执行敏捷运动技能的敏捷策略，以及一个用于防止故障的恢复策略，二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论可达-规避值网络管控，该网络还作为目标函数指导恢复策略，从而在闭环中保障机器人安全。训练过程包括在仿真中学习敏捷策略、可达-规避值网络、恢复策略以及一个外部感知表征网络。这些训练后的模块可通过机载传感与计算直接部署到现实世界中，实现机器人在受限室内外空间中应对静态和动态障碍物的高速无碰撞导航。
英文摘要：
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.
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。
