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.

翻译：本文提出一种数据驱动策略，旨在简化基于模型的控制器的部署流程于腿式机器人硬件平台。该方法利用无模型安全学习算法自动调整控制增益，解决控制公式中简化模型与实际系统之间的不匹配问题。通过在概率安全区域内高效采样优化参数，该方法显著降低与机器人发生危险交互的风险。此外，我们将方法适用范围扩展至将不同步态参数作为上下文，从而构建一种安全、样本高效的探索算法，能够为多种步态模式调优运动控制器。我们通过仿真与硬件实验验证了该方法，结果表明该算法在安全调优多步态基于模型的运动控制器方面获得优越性能。