RK-MPC：用于越野环境中四足运动的残差Koopman模型预测控制
RK-MPC: Residual Koopman Model Predictive Control for Quadruped Locomotion in Offroad Environments
AI总结：
本文提出RK-MPC，一种基于Koopman的的数据驱动模型预测控制框架，通过残差预测器提升预测精度并保持实时可行性，实验证明其在复杂地形中的可靠性和性能。
摘要：
本文介绍了残差Koopman MPC（RK-MPC），一种基于Koopman的、数据驱动的模型预测控制框架，用于四足运动，旨在提高预测精度的同时保持实时可行性。RK-MPC在提升坐标中通过从数据中学习的紧凑线性残差预测器增强名义模板模型，能够系统性地纠正由接触变化和地形扰动引起的模型不匹配，并在多步预测误差上有可证明的界限。所学残差模型嵌入到凸二次规划MPC公式中，产生一个在车载上以500Hz运行的递推控制器，保留了基于优化的控制的结构和约束处理优势。我们评估了RK-MPC在Gazebo模拟和Unitree Go1硬件实验中的表现，展示了在接触扰动、多种步态计划和具有挑战性的越野地形（包括草地、砾石、雪和冰）中的可靠盲运动。我们还通过使用替代可观测字典（包括单项和SE(3)结构基底）的Koopman/EDMD基线进行比较，证明残差修正提高了多步预测和闭环性能，同时减少了对可观测选择的敏感性。总体而言，RK-MPC为数据驱动的四足动物在无结构环境中的预测控制提供了一条实用且硬件验证的途径。见https://sriram-2502.github.io/rk-mpc获取实现视频。
英文摘要：
This paper presents Residual Koopman MPC (RK-MPC), a Koopman-based, data-driven model predictive control framework for quadruped locomotion that improves prediction fidelity while preserving real-time tractability. RK-MPC augments a nominal template model with a compact linear residual predictor learned from data in lifted coordinates, enabling systematic correction of model mismatch induced by contact variability and terrain disturbances with provable bounds on multi-step prediction error. The learned residual model is embedded within a convex quadratic-program MPC formulation, yielding a receding-horizon controller that runs onboard at 500 Hz and retains the structure and constraint-handling advantages of optimization-based control. We evaluate RK-MPC in both Gazebo simulation and Unitree Go1 hardware experiments, demonstrating reliable blind locomotion across contact disturbances, multiple gait schedules, and challenging off-road terrains including grass, gravel, snow, and ice. We further compare against Koopman/EDMD baselines using alternative observable dictionaries, including monomial and $SE(3)$-structured bases, and show that the residual correction improves multi-step prediction and closed-loop performance while reducing sensitivity to the choice of observables. Overall, RK-MPC provides a practical, hardware-validated pathway for data-driven predictive control of quadrupeds in unstructured environments. See https://sriram-2502.github.io/rk-mpc for implementation videos.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
