Residual RL--MPC for Robust Microrobotic Cell Pushing Under Time-Varying Flow

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.

MicroPush: A Simulator and Benchmark for Contact-Rich Cell Pushing and Assembly with a Magnetic Rolling Microrobot

Magnetic rolling microrobots enable gentle manipulation in confined microfluidic environments, yet autonomy for contact-rich behaviors such as cell pushing and multi-target assembly remains difficult to develop and evaluate reproducibly. We present MicroPush, an open-source simulator and benchmark suite for magnetic rolling microrobots in cluttered 2D scenes. MicroPush combines an overdamped interaction model with contact-aware stick--slip effects, lightweight near-field damping, optional Poiseuille background flow, and a calibrated mapping from actuation frequency to free-space rolling speed. On top of the simulator core, we provide a modular planning--control stack with a two-phase strategy for contact establishment and goal-directed pushing, together with a deterministic benchmark protocol with fixed tasks, staged execution, and unified CSV logging for single-object transport and hexagonal assembly. We report success, time, and tracking metrics, and an actuation-variation measure $E_{Δω}$. Results show that controller stability dominates performance under flow disturbances, while planner choice can influence command smoothness over long-horizon sequences via waypoint progression. MicroPush enables reproducible comparison and ablation of planning, control, and learning methods for microscale contact-rich micromanipulation.