State-of-the-art model-based control designs have been shown to be successful in realizing dynamic locomotion behaviors for robotic systems. The precision of the realized behaviors in terms of locomotion performance via fly, hopping, or walking has not yet been well investigated, despite the fact that the difference between the robot model and physical hardware is doomed to produce inaccurate trajectory tracking. To address this inaccuracy, we propose a referencing-steering method to bridge the model-to-real gap by establishing a data-driven input-output (DD-IO) model on top of the existing model-based design. The DD-IO model takes the reference tracking trajectories as the input and the realized tracking trajectory as the output. By utilizing data-driven predictive control, we steer the reference input trajectories online so that the realized output ones match the actual desired ones. We demonstrate our method on the robot PogoX to realize hyper-accurate hopping and flying behaviors in both simulation and hardware. This data-driven reference-steering approach is straightforward to apply to general robotic systems for performance improvement via hyper-accurate trajectory tracking.

翻译：最先进的基于模型的控制设计已被证明在实现机器人系统的动态运动行为方面是成功的。然而，通过飞行、跳跃或行走实现的运动行为在性能精度方面尚未得到充分研究，尽管机器人模型与物理硬件之间的差异注定会导致轨迹跟踪不准确。为了解决这种不准确性，我们提出了一种参考轨迹引导方法，通过在现有基于模型的设计之上建立一个数据驱动的输入输出（DD-IO）模型，来弥合模型与现实的差距。该DD-IO模型以参考跟踪轨迹作为输入，以实际实现的跟踪轨迹作为输出。通过利用数据驱动预测控制，我们在线引导参考输入轨迹，使得实际输出轨迹与真实期望轨迹相匹配。我们在机器人PogoX上验证了我们的方法，在仿真和硬件中均实现了超精确的跳跃和飞行行为。这种数据驱动的参考轨迹引导方法易于应用于通用机器人系统，通过超精确的轨迹跟踪来提高其性能。