DISCO：基于扩散策略与约束修复的语言引导操作
DISCO: Language-Guided Manipulation with Diffusion Policies and Constrained Inpainting
AI总结：
DISCO框架利用现成视觉语言模型将自然语言指令转化为3D关键帧，并通过约束修复与优化策略引导扩散策略，在零样本开放词汇机器人操作中实现优于微调方法的泛化性能。
摘要：
扩散策略在生成建模中展现出强大性能，使其有望用于自然语言指令引导的机器人操作。然而，由于机器人演示数据集的稀缺和高成本，将语言条件扩散策略泛化到日常场景中的开放词汇指令仍具挑战性。为此，我们提出DISCO框架，利用现成的视觉语言模型（VLM）桥接自然语言理解与高性能扩散策略。DISCO使用VLM将语言任务描述转化为可执行的3D关键帧，随后通过约束修复引导扩散过程。然而，当VLM生成的关键帧不准确时，严格遵循这些关键帧会降低性能。为缓解这一问题，我们引入一种修复优化策略，在关键帧遵循与来自训练数据的学习运动先验之间取得平衡。仿真和真实环境中的实验结果表明，DISCO优于传统的微调语言条件策略，在零样本、开放词汇操作任务中实现了更优的泛化能力。
英文摘要：
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.
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