Function-Preserving Data Generation for Zero-Shot Real-to-Sim-to-Real Manipulation

2026-09-16

AI Summary

为解决机器人学习中生成几何多样且物理有效的数据难题，提出了一种功能保持的实到仿真再到实框架，通过约束引导的网格变形增强物体几何形状，并结合任务姿态和碰撞代理的一致转移。

Abstract

Robotic data generation is a promising paradigm for scaling robot learning without collecting large-scale real-world data. However, generating geometrically diverse yet physically valid data for contact-rich tasks remains challenging, especially when success depends on precise geometric interfaces. Standard shape augmentation methods often distort task-critical interfaces, resulting in invalid contact relationships, e.g., fit mismatches or interpenetration, rendering downstream interactions infeasible. To address these limitations, we propose a function-preserving Real-to-Sim-to-Real framework that generates synthetic demonstrations from reconstructed assets without teleoperated source trajectories. Our method augments task-relevant object geometries through constraint-guided mesh deformation, together with physically consistent transfer of task poses and collision proxies. Visual domain randomization is further applied during simulation rollouts, enabling robust zero-shot policy deployment without real-world fine-tuning. Extensive experiments in both real-world and simulation settings demonstrate that our method enables robust generalization across unseen object geometries and diverse visual conditions in contact-rich and long-horizon tasks. Our method provides a practical path toward scalable robot learning for contact-rich tasks via shape deformation.

Problem

• robotic data generation
• geometric diversity
• physically valid data
• contact-rich tasks
• task-critical interfaces

Innovation

• function-preserving
• constraint-guided mesh deformation
• visual domain randomization
• zero-shot policy deployment