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. 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. Experiments 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.

翻译：在微流控流场中进行接触密集的微操作具有挑战性，因为微小的扰动就可能破坏推送接触并引发显著的横向漂移。我们研究了一种磁性滚动微机器人在时变泊肃叶流场下跟踪由航点采样的参考曲线进行平面细胞推送的问题。我们提出了一种混合控制器，它通过由SAC训练得到的残差策略来增强一个标称的模型预测控制器。该策略输出一个有界的二维速度修正量，该修正量受接触门控，因此残差动作仅在机器人-细胞接触期间应用，从而保留了可靠的趋近行为并稳定了学习过程。所有方法共享相同的执行接口和速度包络，以确保公平比较。实验表明，在非平稳流场下，该方法相较于纯模型预测控制器和PID控制器，在鲁棒性和跟踪精度方面均有提升，并且能够从训练所用的三叶草曲线泛化到未见的圆形和方形轨迹。通过对残差界限的扫描，确定了一个中间修正限值作为最佳权衡点，我们在所有基准测试中均使用了该限值。