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，旨在简化模型控制器在腿式机器人硬件平台上的部署。我们的方法利用无模型安全学习算法自动化控制增益的调优过程，以解决控制公式中使用的简化模型与真实系统之间的失配问题。通过在概率安全区域内高效采样优化参数，该方法显著降低了与机器人危险交互的风险。此外，我们将方法的适用性拓展至将不同步态参数作为上下文，进而开发出一种安全且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。通过仿真与硬件实验验证，我们证明该算法能够安全地调优基于模型的运动控制器，并在多种步态下获得优越性能。