As learning-based methods for legged robots rapidly grow in popularity, it is important that we can provide safety assurances efficiently across different controllers and environments. Existing works either rely on a priori knowledge of the environment and safety constraints to ensure system safety or provide assurances for a specific locomotion policy. To address these limitations, we propose an observation-conditioned reachability-based (OCR) safety-filter framework. Our key idea is to use an OCR value network (OCR-VN) that predicts the optimal control-theoretic safety value function for new failure regions and dynamic uncertainty during deployment time. Specifically, the OCR-VN facilitates rapid safety adaptation through two key components: a LiDAR-based input that allows the dynamic construction of safe regions in light of new obstacles and a disturbance estimation module that accounts for dynamics uncertainty in the wild. The predicted safety value function is used to construct an adaptive safety filter that overrides the nominal quadruped controller when necessary to maintain safety. Through simulation studies and hardware experiments on a Unitree Go1 quadruped, we demonstrate that the proposed framework can automatically safeguard a wide range of hierarchical quadruped controllers, adapts to novel environments, and is robust to unmodeled dynamics without a priori access to the controllers or environments - hence, "One Filter to Deploy Them All". The experiment videos can be found on the project website.

翻译：随着基于学习的足式机器人方法迅速普及，我们亟需一种能高效保障不同控制器与环境安全性的机制。现有研究要么依赖对环境与安全约束的先验知识来确保系统安全，要么仅针对特定运动策略提供保障。为克服这些局限，本文提出一种观测条件可达性安全过滤框架。其核心思想是采用OCR值网络，该网络能在部署阶段针对新出现的故障区域和动态不确定性，预测最优控制理论安全值函数。具体而言，OCR-VN通过两个关键组件实现快速安全适配：基于激光雷达的输入模块可动态构建新障碍物环境下的安全区域，以及扰动估计模块用于处理实际场景中的动力学不确定性。预测的安全值函数用于构建自适应安全过滤器，在必要时覆盖四足机器人主控制器以维持安全性。通过在Unitree Go1四足机器人上的仿真研究与硬件实验，我们证明该框架能自动保障多种分层式四足控制器，适应新环境，并对未建模动力学具有鲁棒性——整个过程无需预先获取控制器或环境信息，故称“一种通用部署过滤器”。实验视频可在项目网站查看。