MicroPush: A Lightweight Simulator for Sim-to-Real Microrobotic Pushing and Assembly

Abstract:Developing learning-based autonomy for microrobotic manipulation is difficult because physical trials are slow, variable, and poorly suited to large-scale training. We present MicroPush, a lightweight simulation and benchmarking environment for pushing and assembly with magnetic rolling microrobots. The simulator combines overdamped planar dynamics with calibrated rolling actuation, reduced-order contact handling, short-range interaction corrections, and optional background flow, while providing common interfaces for planning, model-based control, reinforcement learning (RL), interactive visualization, and headless evaluation. In no-render tests, MicroPush remains substantially faster than real time, reaching 37.0x real time with 100 passive particles. We further evaluate the simulator through zero-shot sim-to-real policy transfer. A Residual RL-MPC policy is trained entirely in MicroPush, frozen after simulation training, and deployed on the physical system without real-world policy fine-tuning. A 20 {\mu}m magnetic microrobot pushes a similarly sized silica particle along a figure-eight trajectory with 1.91 {\mu}m median and 4.65 {\mu}m 95th-percentile path error, and the same autonomy stack performs sequential line, triangle, and six-particle hexagonal assembly. These results show that a deliberately reduced-order simulator can provide an efficient training and development substrate for sim-to-real microrobotic autonomy.

Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2602.23607 [cs.RO]

(or arXiv:2602.23607v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2602.23607 Focus to learn more arXiv-issued DOI via DataCite

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