analiza aiqbench
This paper proposes using renormalization techniques—originally from high-energy physics—to address the sim-to-real gap in robotics by deriving effective parameters that compensate for simulator limitations. The authors demonstrate this analytically through PD control adjustments and apply it to complex tasks like dynamic rope manipulation, offering a theoretical framework distinct from building more accurate physical models.
Effective Parameters, Real Behavior: Renormalization for Robotics -- From Infinite Electron Mass to Sim-to-Real Gap
arXiv:2607.24079v1 Announce Type: new 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.
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.
Robotics (cs.RO); High Energy Physics - Theory (hep-th)
arXiv:2607.24079 [cs.RO]
(or arXiv:2607.24079v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.24079 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 230 cuvinte
Subiecte conexe

Generalization and infinite width
LessWrong · 1 aug. 2026
Links #3: 2026/06 Part 1
LessWrong · 15 iun. 2026

The Alignment Problem: Computer Science Meets Analytic Philosophy
LessWrong · 18 oct. 2026

Runway’s WorldPrompt and the Engineering of Real-Time Worlds
Latent Space · 25 sept. 2026