FITR: From Articulated Object Images to Interactive 3D Assets for Real-World Manipulation

Collecting real-world training data for articulated object manipulation is costly, motivating scalable data generation in simulation. Recent advances in 3D asset generation have improved visual fidelity and geometric quality, but supporting robot interaction also requires appropriate physical dimensions, contact points and directions, and joint dynamics. To this end, we introduce FITR, a pipeline that constructs interactive 3D assets from images of articulated objects for policy learning in simulation and zero-shot deployment on real robots. We construct FITR-Assets by augmenting articulated assets with absolute scale, affordances, and calibrated joint drive parameters, and evaluate annotation quality on FITR-Bench. Using these assets, we combine motion planning with domain randomization to automatically collect approximately 60k manipulation demonstrations in Isaac Sim. Then, we fine-tune vision-language-action (VLA) policies via imitating the demonstrations and RL-driven simulated post-training, enabling zero-shot transfer to real world. Through systematic ablation studies and comparative experiments in simulation and on real robots, we demonstrate that FITR enables zero-shot sim-to-real transfer for articulated object manipulation. We release FITR-Assets, the simulation demonstration dataset, policy checkpoints, and source code.