Effective Parameters, Real Behavior: Renormalization for Robotics -- From Infinite Electron Mass to Sim-to-Real Gap

2026-07-27

AI Summary

This work addresses the sim-to-real gap in robotics by proposing a novel approach inspired by renormalization concepts from physics. The method systematically incorporates effective parameters that depend on simulation resolution to absorb neglected high-order dynamical details. By integrating PD control analysis, dynamic rope manipulation, and underwater swimming modeling, the authors establish an identification pipeline for these effective parameters based on observable quantities. Theoretical analysis reveals that, under finite simulation frequencies, proportional feedback effectively tunes the derivative gain while derivative feedback modulates effective inertia. This framework successfully reproduces real-world system behaviors in complex dynamic tasks, offering a new paradigm for bridging the sim-to-real gap.

Abstract

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.

Problem

• sim-to-real gap
• renormalization
• effective parameters
• robotics
• simulation

Innovation

• renormalization
• sim-to-real gap
• effective parameters
• robotics
• PD control