外观
论文arXiv 2610.1619916 天前
MSC：具有不确定性感知 NMPC 和基于 RL 的执行层的多阶段脚轮感知控制框架
原文链接：MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer（arXiv 2610.16199 · 发布日期：2026-10-12）
MSC: Multi-Stage Caster-aware Control Framework with Uncertainty-Aware NMPC and RL-based Execution Layer
针对脚轮错位导致的规划执行偏差，MSC结合不确定性感知NMPC与RL执行层，并在Isaac Sim和真实机器人上验证。
核心要点
- 自由旋转脚轮在低速或高曲率机动中易错位，造成强接触非线性和指令—实际运动偏差。
- MSC采用分层规划执行一体框架，CU-MPC用预测不确定性调度脚轮感知代价与输入正则。
- RL执行层在部分可观测下估计并预测脚轮状态，将高层速度命令转为可执行轮速。
- Isaac Sim和真机结果显示，MSC优于解耦补偿方法，提升估计精度、减小错位并增强跟踪鲁棒性。
验证范围与局限：验证范围为Isaac Sim与真实机器人上的脚轮敏感机动。
英文摘要 (Abstract)
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.
作者：Jinho Park, Jeongtae Lee, Jeonghyeok Lim, Keun Ha Choi, Kyung-Soo Kim
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