用学习的接触残差提升喷气助跑机器人的运动稳定性。
Thruster-Enhanced Locomotion: A Decoupled Model Predictive Control with Learned Contact Residuals
- 分控架构：腿部用位置控制，喷口用带学习残差的模型预测控制。
- 加入学习残差后，抗推倒和猫步行走更稳定。
- 适合研究喷气辅助机器人运动控制的工程师与研究员。
东北大学研发的Husky Carbon机器人平台用于探索姿态操控与推力矢量的统一控制。与传统四足机器人不同，其关节执行器与喷射装置协同提供更强的控制能力，支持喷气辅助窄道行走。虽然统一的模型预测控制（MPC）框架理论上可同时优化地面反作用力与喷射力，但受限于轻量化执行器的低扭矩控制带宽。为此，我们提出一种解耦控制架构：采用Raibert型控制器基于位置控制实现腿部运动，同时通过引入学习的接触残差动力学（CRD）的MPC调节喷射装置，以捕捉腿-地冲击动态。该分离设计规避了扭矩控制速率瓶颈，同时保留了喷射MPC对腿-地冲击动力学的显式建模能力。通过仿真与硬件实验验证，加入CRD的解耦控制器在抗推倒恢复和猫步行走等任务中表现出更稳定的性能，显著优于无CRD的对照组。
原文摘要 · Abstract (English)
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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