用强化学习修正模型预测控制，提升微机器人在变化流体中推细胞的稳定性。
Residual RL-MPC for Robust Microrobotic Cell Pushing Under Time-Varying Flow
- 融合强化学习残差策略与模型预测控制，仅在接触时修正运动方向。
- 在非稳态流中追踪精度提升，能泛化到未训练的圆形和方形路径。
- 适合需高鲁棒性微操控的生物医学应用，如细胞运输与组装。
在微流体环境中进行高接触密度的微操控极具挑战，因微小扰动易导致推挤接触中断并引发显著侧向漂移。本文研究磁驱动滚动微机器人在时间变化的泊肃叶流中沿采样点轨迹进行二维细胞推挤的任务。提出一种混合控制器，将标准模型预测控制（MPC）与通过软动作价值函数（SAC）训练的残差策略结合。该策略输出有界二维速度修正量，且仅在机器人-细胞接触时激活，从而保持可靠接近行为并稳定学习过程。所有方法采用相同的执行接口与速度范围以保证公平对比。仿真结果表明，在非平稳流下，该方法相比纯MPC与PID控制展现出更优的鲁棒性与轨迹跟踪精度；模型从三叶草形训练轨迹可泛化至未见过的圆形与方形路径。残差修正幅度扫查实验识别出一个适中修正上限为最佳折衷方案，并用于所有基准测试。
原文摘要 · Abstract (English)
Contact-rich micromanipulation in microfluidic flow is challenging because small disturbances can break pushing contact and induce large lateral drift. We study planar cell pushing with a magnetic rolling microrobot that tracks a waypoint-sampled reference curve under time-varying Poiseuille flow in simulation. We propose a hybrid controller that augments a nominal MPC with a learned residual policy trained by SAC. The policy outputs a bounded 2D velocity correction that is contact-gated, so residual actions are applied only during robot-cell contact, preserving reliable approach behavior and stabilizing learning. All methods share the same actuation interface and speed envelope for fair comparisons. Simulation results show improved robustness and tracking accuracy over pure MPC and PID under nonstationary flow, with generalization from a clover training curve to unseen circle and square trajectories. A residual-bound sweep identifies an intermediate correction limit as the best trade-off, which we use in all benchmarks.
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