MicroPush：一种用于接触密集细胞推动和组装的模拟器和基准测试平台，配备磁性滚动微机器人
MicroPush: A Lightweight Simulator for Sim-to-Real Microrobotic Pushing and Assembly
AI总结：
MicroPush提供了一个开源的模拟器和基准测试平台，用于评估磁性滚动微机器人在接触密集任务中的自主操作能力，包括细胞推动和多目标组装。
摘要：
磁性滚动微机器人能够在受限的微流体环境中实现温和的操作，但自主执行接触密集行为如细胞推动和多目标组装仍然难以开发和可重复评估。我们提出了MicroPush，一种开源的模拟器和基准测试套件，用于磁性滚动微机器人在拥挤的2D场景中的应用。MicroPush结合了过阻尼相互作用模型、具有接触意识的粘滞效应、轻量级近场阻尼、可选的泊肃叶背景流以及一个校准的从驱动频率到自由空间滚动速度的映射。在模拟器核心之上，我们提供了一个模块化的规划-控制堆栈，具有两阶段策略用于接触建立和目标导向推动，以及一个确定性的基准测试协议，具有固定任务、分阶段执行和统一的CSV日志记录，用于单对象运输和六边形组装。我们报告了成功率、时间、跟踪指标以及一个驱动变化度量$E_{Δω}$。结果表明，在流扰动下，控制器稳定性主导性能，而规划器的选择可以通过路径点进展影响长时间序列中的命令平滑度。MicroPush实现了微尺度接触密集微操作中规划、控制和学习方法的可重复比较和消融。
英文摘要：
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 μm magnetic microrobot pushes a similarly sized silica particle along a figure-eight trajectory with 1.91 μm median and 4.65 μm 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
