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

TL;DR

MicroPush is an open-source simulator and benchmark suite designed for contact-rich manipulation tasks with magnetic rolling microrobots in microfluidic environments, enabling reproducible evaluation of planning and control strategies.

Contribution

It introduces a comprehensive simulation environment and benchmark protocol for contact-rich micromanipulation with magnetic microrobots, facilitating reproducible research and comparison.

Findings

Controller stability is crucial under flow disturbances.

Planner choice affects command smoothness over long sequences.

MicroPush enables reproducible evaluation of manipulation strategies.

Abstract

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 $E_{\Delta\omega}$. 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.