Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
This addresses the challenge of safely deploying model-based controllers on legged robotic hardware, though it is an incremental improvement over existing safe learning methods.
The paper tackles the problem of automating control gain tuning for legged robots by using a safe Bayesian optimization approach to address model-reality mismatches, resulting in superior performance for multiple gaits in simulation and hardware experiments.
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.