In this paper, we present a data-driven strategy to simplify 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：本文提出了一种数据驱动策略，以简化基于模型的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调节控制增益，解决控制公式中使用的简化模型与真实系统之间的不匹配问题。该方法通过样本高效地在可能安全区域内优化参数，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扩展到将不同步态参数作为上下文纳入，形成一种安全、样本高效的探索算法，能够为多种步态模式调整运动控制器。我们通过仿真和硬件实验验证了该方法，结果表明该算法在安全地调整基于模型的运动控制器以适配多种步态方面表现出卓越性能。