DiagGen: Agentic Generation of Deformable Assets with Sim-based Diagnostics for Robotic Simulation

2026-09-19

AI Summary

本文提出DiagGen框架，通过生成-模拟-诊断-优化循环，将野外图像转化为可用于机器人仿真的可变形资产，提高生成资产的质量。

Abstract

While simulation-ready deformable assets are essential for in-silico robotic manipulation tasks, existing generation frameworks typically assess physical plausibility after generation, leaving an object's simulated response unused as feedback for repairing upstream errors. We present DiagGen, an agentic framework that turns a single in-the-wild image into a simulation-ready deformable asset through a generate--simulate--diagnose--refine loop. DiagGen constructs part-aware geometry and material parameters, then uses a VLM (vision-language model)-based agent to select semantically informative regions, probe them in a physics simulator, observe material responses, and route evidence-backed repair cues to the responsible generation stage. Experiments on 40 assets show that diagnostics provides useful repair cues and can moderately improve the quality of generated deformable assets. Finally, we show that unlike assets generated from visual foundation models which may not be simulatable, DiagGen-generated deformables can be directly dropped into a high-fidelity physical simulator for the planning and simulation of contact-rich pick-and-place tasks. The project's website is https://diaggen.github.io/.

Problem

• deformable assets
• simulation
• physical plausibility
• generation framework
• feedback

Innovation

• generate--simulate--diagnose--refine loop
• VLM-based agent
• physics simulator
• deformable assets
• contact-rich pick-and-place tasks