SimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models
Overview
This research introduces SimForcing, a simulation-guided framework that transfers motion knowledge from a simulation teacher to real-domain robot world models using latent-motion distillation and multi-block simulation conditioning. The method balances internalized motion priors with simulation guidance via classifier-free guidance, enabling the generation of realistic videos without external embodied pretraining. Evaluated on Bridge and InternData-A1 datasets, SimForcing achieves superior performance in PSNR, SSIM, LPIPS, and FVD metrics compared to other methods. Additionally, initializing a vision-language-action model with the trained world model improves success rates in the LIBERO benchmark, demonstrating its utility for downstream policy learning tasks.
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- arXiv Robotics — research abstractsSignalSimForcing: Distilling Simulation Motion Priors into Real-Domain Robot World Models
This research introduces SimForcing, a simulation-guided framework that transfers motion knowledge from a simulation teacher to real-domain robot world models using latent-motion distillation and multi-block simulation conditioning. The method balances internalized motion priors with simulation guidance via classifier-free guidance, enabling the generation of realistic videos without external embodied pretraining. Evaluated on Bridge and InternData-A1 datasets, SimForcing achieves superior performance in PSNR, SSIM, LPIPS, and FVD metrics compared to other methods. Additionally, initializing a vision-language-action model with the trained world model improves success rates in the LIBERO benchmark, demonstrating its utility for downstream policy learning tasks.
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