MSC框架：不确定性感知NMPC结合强化学习，解决工业AMR万向轮控制偏移问题
MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer
机器人控制方向的新论文，专门治AMR万向轮低速打滑错位的老毛病，NMPC加强化学习双层配合，仿真和实机都验证过了。
论文提出Multi-Stage Caster-aware Control（MSC），一个规划-执行一体的分层控制框架，针对工业AMR/SDV中万向轮（caster）在低速或高曲率机动时的错位非线性问题。框架包含两个核心机制：不确定性感知NMPC（CU-MPC）根据预测不确定性动态调度万向轮感知代价和输入正则化，抑制过于激进的机动；强化学习执行层在部分可观测条件下估计并预测万向轮状态，将高层速度指令转换为适配当前接触条件的轮速。在Isaac Sim仿真和真实机器人上的验证显示，MSC相比现有解耦补偿方法显著提升了万向轮状态估计精度，并降低了错位幅度。
MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer
Freely swiveling casters are widely adopted in industrial AMRs/SDVs for their mechanical simplicity and load capacity. However, caster misalignment during low-speed or high-curvature maneuvers induces strong contact nonlinearities, widening the planning-execution gap between commanded $(v, ω)$ and realized motion. Existing caster-aware strategies are highly sensitive to state-estimation reliability, causing unstable execution in uncertain transition regimes. To address this, we present Multi-Stage Caster-aware Control (MSC), a planning-execution integrated hierarchical framework featuring two core mechanisms. First, an uncertainty-aware NMPC (CU-MPC) dynamically schedules caster-aware costs and input regularization using predictive uncertainty to suppress overly aggressive maneuvers. Second, a reinforcement learning-based execution layer estimates and predicts caster states under partial observability, transforming high-level velocity commands into physically executable wheel speeds adapted to current contact conditions. Validations in Isaac Sim and real robots demonstrate that MSC offers a clear comparative advantage over existing decoupled compensation approaches by significantly improving caster-state estimation accuracy, minimizing misalignment, and enhancing tracking robustness in caster-sensitive maneuvers.