  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.
