DISCO: Language-Guided Manipulation with Diffusion Policies and Constrained Inpainting

TL;DR

DISCO is a novel framework that combines vision-language models and diffusion policies to enable robots to understand and execute open-vocabulary manipulation tasks guided by natural language, improving generalization and zero-shot performance.

Contribution

DISCO introduces a method to translate natural language instructions into 3D keyframes using VLMs and guides diffusion policies through constrained inpainting with an optimization strategy, enhancing open-vocabulary manipulation.

Findings

Outperforms fine-tuned policies in zero-shot tasks

Demonstrates effective generalization in real-world scenarios

Balances keyframe adherence with learned motion priors

Abstract

Diffusion policies have demonstrated strong performance in generative modeling, making them promising for robotic manipulation guided by natural language instructions. However, generalizing language-conditioned diffusion policies to open-vocabulary instructions in everyday scenarios remains challenging due to the scarcity and cost of robot demonstration datasets. To address this, we propose DISCO, a framework that leverages off-the-shelf vision-language models (VLMs) to bridge natural language understanding with high-performance diffusion policies. DISCO translates linguistic task descriptions into actionable 3D keyframes using VLMs, which then guide the diffusion process through constrained inpainting. However, enforcing strict adherence to these keyframes can degrade performance when the VLM-generated keyframes are inaccurate. To mitigate this, we introduce an inpainting optimization strategy that balances keyframe adherence with learned motion priors from training data. Experimental results in both simulated and real-world settings demonstrate that DISCO outperforms conventional fine-tuned language-conditioned policies, achieving superior generalization in zero-shot, open-vocabulary manipulation tasks.