Safety concerns during the operation of legged robots must be addressed to enable their widespread use. Machine learning-based control methods that use model-based constraints provide promising means to improve robot safety. This study presents a modular safety filter to improve the safety of a legged robot, i.e., reduce the chance of a fall. The prerequisite is the availability of a robot that is capable of locomotion, i.e., a nominal controller exists. During locomotion, terrain properties around the robot are estimated through machine learning which uses a minimal set of proprioceptive signals. A novel deep-learning model utilizing an efficient transformer architecture is used for the terrain estimation. A quadratic program combines the terrain estimations with inverse dynamics and a novel exponential control barrier function constraint to filter and certify nominal control signals. The result is an optimal controller that acts as a filter. The filtered control signal allows safe locomotion of the robot. The resulting approach is generalizable, and could be transferred with low effort to any other legged system.

翻译：腿式机器人运行期间的安全问题必须得到解决，才能推动其广泛应用。基于模型约束的机器学习控制方法为提高机器人安全性提供了有前景的方案。本研究提出一种模块化安全过滤器，旨在提升腿式机器人的安全性，即降低摔倒概率。其前提条件是机器人具备运动能力，即存在一个标称控制器。在运动过程中，机器人周围的地形属性通过机器学习进行估计，该方法仅使用最少的本体感知信号。一种采用高效Transformer架构的新型深度学习模型被用于地形估计。通过二次规划将地形估计与逆动力学及新型指数控制障碍函数约束相结合，对标称控制信号进行滤波与认证。最终获得充当滤波器的优化控制器。经滤波的控制信号使机器人能够安全运动。该方法具有泛化性，可低成本迁移至任何其他腿式系统。