  This paper presents a data-driven strategy to streamline the deployment of
model-based controllers in legged robotic hardware platforms. Our approach
leverages a model-free safe learning algorithm to automate the tuning of
control gains, addressing the mismatch between the simplified model used in the
control formulation and the real system. This method substantially mitigates
the risk of hazardous interactions with the robot by sample-efficiently
optimizing parameters within a probably safe region. Additionally, we extend
the applicability of our approach to incorporate the different gait parameters
as contexts, leading to a safe, sample-efficient exploration algorithm capable
of tuning a motion controller for diverse gait patterns. We validate our method
through simulation and hardware experiments, where we demonstrate that the
algorithm obtains superior performance on tuning a model-based motion
controller for multiple gaits safely.
