WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn from failures and perform self-corrections. Reinforcement learning (RL) addresses these through self-improving interactions with the physical environment, but suffers from high sample complexity on real robots. We introduce World-Model-based Policy Optimization (WMPO), a principled framework for on-policy VLA RL without interacting with the real environment. In contrast to widely used latent world models, WMPO focuses on pixel-based predictions that align the "imagined" trajectories with the VLA features pretrained with web-scale images. Crucially, WMPO enables the policy to perform on-policy GRPO that provides stronger performance than the often-used off-policy methods. Extensive experiments in both simulation and real-robot settings demonstrate that WMPO (i) substantially improves sample efficiency, (ii) achieves stronger overall performance, (iii) exhibits emergent behaviors such as self-correction, and (iv) demonstrates robust generalization and lifelong learning capabilities.
69154c35a1b06ca3cc8134dc
Fangqi Zhu
69154c35a1b06ca3cc8134dd
Zhengyang Yan
69154c35a1b06ca3cc8134de
Zicong Hong
69154c35a1b06ca3cc8134df
Quanxin Shou
69154c35a1b06ca3cc8134e0
Xiao Ma
69154c35a1b06ca3cc8134e1
Song Guo
WMPO: World Model-based Policy Optimization for Vision-Language-Action Models
Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation, but their reliance on expert demonstrations limits their ability to learn from failures and perform self-corrections. Reinforcement learning (RL) addresses these through self-improving interactions with the physical environment, but suffers from high sample complexity on real robots. We introduce World-Model-based Policy Optimization (WMPO), a principled framework for on-policy VLA RL without interacting with the real environment. In contrast to widely used latent world models, WMPO focuses on pixel-based predictions that align the "imagined" trajectories with the VLA features pretrained with web-scale images. Crucially, WMPO enables the policy to perform on-policy GRPO that provides stronger performance than the often-used off-policy methods. Extensive experiments in both simulation and real-robot settings demonstrate that WMPO (i) substantially improves sample efficiency, (ii) achieves stronger overall performance, (iii) exhibits emergent behaviors such as self-correction, and (iv) demonstrates robust generalization and lifelong learning capabilities.
WMPO, a pixel-based world-model framework for VLA RL, enhances sample efficiency, performance, self-correction, and generalization in robotic manipulation tasks.
World-Model-based Policy Optimization WMPO pixel-based predictions latent world models on-policy GRPO off-policy methods sample efficiency emergent behaviors self-correction robust generalization lifelong learning