Diffusion policies have demonstrated robust performance in generative modeling, prompting their application in robotic manipulation controlled via language descriptions. In this paper, we introduce a zero-shot, open-vocabulary diffusion policy method for robot manipulation. Using Vision-Language Models (VLMs), our method transforms linguistic task descriptions into actionable keyframes in 3D space. These keyframes serve to guide the diffusion process via inpainting. However, naively enforcing the diffusion process to adhere to the generated keyframes is problematic: the keyframes from the VLMs may be incorrect and lead to out-of-distribution (OOD) action sequences where the diffusion model performs poorly. To address these challenges, we develop an inpainting optimization strategy that balances adherence to the keyframes v.s. the training data distribution. Experimental evaluations demonstrate that our approach surpasses the performance of traditional fine-tuned language-conditioned methods in both simulated and real-world settings.
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