推进器增强运动：一种带有学习接触残差的解耦模型预测控制
Thruster-Enhanced Locomotion: A Decoupled Model Predictive Control with Learned Contact Residuals
AI总结：
针对四足机器人推进器辅助行走，提出解耦控制架构，用Raibert控制器管理腿部，MPC配合学习接触残差控制推进器，提升抗推和猫步稳定性。
摘要：
Husky Carbon是由东北大学开发的一款机器人，作为研究平台，旨在探索姿态操纵与推力矢量控制的统一。与传统四足机器人不同，其关节执行器和推进器提供了增强的控制权限，有助于实现推进器辅助的窄路径行走。虽然统一的模型预测控制（MPC）框架能够同时优化地面反作用力和推进器力，理论上可以解决这一控制问题，但其可行性受到系统轻量级执行器低扭矩控制带宽的限制。为克服这一挑战，我们提出了一种解耦控制架构：Raibert型控制器采用基于位置的控制来管理腿部运动，而MPC则调节由学习接触残差动力学（CRD）增强的推进器，以考虑腿-地冲击。这种分离绕过了扭矩控制速率瓶颈，同时保留推进器MPC，通过学习残差显式考虑腿-地冲击动力学。我们通过仿真和硬件实验验证了该方法，结果表明，与不带CRD的解耦控制器相比，带有CRD的解耦控制架构在推力恢复和猫式行走步态方面表现出更稳定的行为。
英文摘要：
Husky Carbon, a robot developed by Northeastern University, serves as a research platform to explore unification of posture manipulation and thrust vectoring. Unlike conventional quadrupeds, its joint actuators and thrusters enable enhanced control authority, facilitating thruster-assisted narrow-path walking. While a unified Model Predictive Control (MPC) framework optimizing both ground reaction forces and thruster forces could theoretically address this control problem, its feasibility is limited by the low torque-control bandwidth of the system's lightweight actuators. To overcome this challenge, we propose a decoupled control architecture: a Raibert-type controller governs legged locomotion using position-based control, while an MPC regulates the thrusters augmented by learned Contact Residual Dynamics (CRD) to account for leg-ground impacts. This separation bypasses the torque-control rate bottleneck while retaining the thruster MPC to explicitly account for leg-ground impact dynamics through learned residuals. We validate this approach through both simulation and hardware experiments, showing that the decoupled control architecture with CRD performs more stable behavior in terms of push recovery and cat-like walking gait compared to the decoupled controller without CRD.
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