用有效参数补偿模拟器缺失物理，让仿真更贴近真实行为。
Effective Parameters, Real Behavior: Renormalization for Robotics -- From Infinite Electron Mass to Sim-to-Real Gap
- 用随分辨率变化的有效参数吸收模拟器遗漏的物理细节
- 在有限仿真频率下，PD控制中有效参数会改变惯性与微分增益
- 适用于动态绳索操控和水下游泳等复杂系统建模
弥合仿真到现实的差距是机器人学的核心挑战，主流方法是构建越来越精确的模拟器。本文提出一种基于重整化的新思路：使用依赖于分辨率的有效参数，吸收模拟器忽略的细节，重现真实行为。这些参数可能与实测物理值不同，因其需补偿模拟器缺失的部分。我们从理论上分析了在有限仿真频率下的比例-微分（PD）控制，发现比例反馈会改变有效微分增益，微分反馈会改变有效惯性。随后，我们以此视角解释了动态绳索操控与水下游泳现象。最后，给出了选择可观测量、识别缺失物理并确定有效参数的实用流程。重整化为机器人学提供了一条跨越仿真-现实鸿沟的互补路径：有效参数，真实行为。
原文摘要 · Abstract (English)
Bridging the sim-to-real gap is a central problem in robotics, and the prevailing approach is to build increasingly accurate simulators. Here, we propose another approach based on renormalization: using effective, resolution-dependent parameters to absorb details omitted by the simulator and reproduce real behavior. These parameters may differ from measured physical values because they compensate for what the simulator leaves out. We demonstrate this mechanism analytically for proportional--derivative (PD) control at finite simulation frequency, where proportional feedback changes the effective derivative gain and derivative feedback changes the effective inertia. We then interpret dynamic rope manipulation and underwater swimming through the same perspective. Finally, we present a practical procedure for choosing observables, identifying omitted physics, and determining effective parameters. Renormalization offers robotics a complementary path across the sim-to-real gap: effective parameters, real behavior.
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