Sim, Yet Same: Physics-Aligned Simulator as Zero-Shot Data Scaler in Deformable Worlds

Abstract

Robotic manipulation with deformable objects represents adata-intensive regime in embodied learning, where shape, contact, andtopology co-evolve in ways that far exceed the variability of rigids. Al-though simulation promises relief from the cost of real-world data acqui-sition, prevailing sim-to-real pipelines remain rooted in rigid-body ab-stractions, producing mismatched geometry, fragile soft dynamics, andmotion primitives poorly suited for cloth interaction. We posit that simu-lation fails not for being synthetic, but for being ungrounded. To addressthis, we introduce SIM1, a physics-aligned real-to-sim-to-real data enginethat grounds simulation in the physical world. Given limited demon-strations, the system digitizes scenes into metric-consistent twins, cali-brates deformable dynamics through elastic modeling, and expands be-haviors via diffusion-based trajectory generation with quality filtering.vision with near-demonstration fidelity. Experiments show that policiestrained on purely synthetic data achieve parity with real-data baselinesat a 1:15 equivalence ratio, while delivering 90% zero-shot success and50% generalization gains in real-world deployment. These results vali-date physics-aligned simulation as scalable supervision for deformablemanipulation and a practical pathway for data-efficient policy learning.