Reference-Steering via Data-Driven Predictive Control for Hyper-Accurate Robotic Flying-Hopping Locomotion

TL;DR

This paper introduces a data-driven reference-steering method that enhances the accuracy of robotic flying-hopping locomotion by bridging the model-to-real gap, demonstrated on the PogoX robot in simulation and hardware.

Contribution

It proposes a novel data-driven input-output model combined with predictive control to improve trajectory tracking accuracy in dynamic robotic locomotion.

Findings

Achieved hyper-accurate hopping and flying behaviors on PogoX

Effective in both simulation and real hardware

Applicable to various robotic systems for performance enhancement

Abstract

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.