Building generalist robot policies that can handle diverse tasks in open-ended environments is a central challenge in robotics. To leverage knowledge from large-scale pretraining, prior work (VLA) has typically built generalist policies either on top of vision-language understanding models (VLMs) or generative models. However, both semantic understanding from vision-language pretraining and visual dynamics modeling from visual-generation pretraining are crucial for embodied robots. Recent unified models of generation and understanding have demonstrated strong capabilities in both comprehension and generation through large-scale pretraining. We posit that robotic policy learning can likewise benefit from the combined strengths of understanding, planning, and continuous future representation learning. Building on this insight, we introduce UniCoD, which acquires the ability to dynamically model high-dimensional visual features through pretraining on over 1M internet-scale instructional manipulation videos. Subsequently, UniCoD is fine-tuned on data collected from the robot embodiment, enabling the learning of mappings from predictive representations to action tokens. Extensive experiments show our approach consistently outperforms baseline methods in terms of 9\% and 12\% across simulation environments and real-world out-of-distribution tasks.

翻译：构建能够处理开放环境中多样化任务的通用机器人策略是机器人学中的核心挑战。为利用大规模预训练知识，先前研究（VLA）通常基于视觉语言理解模型（VLMs）或生成模型构建通用策略。然而，来自视觉语言预训练的语义理解与来自视觉生成预训练的视觉动态建模对于具身机器人均至关重要。近期统一生成与理解的模型已通过大规模预训练展现出在理解与生成两方面的强大能力。我们认为机器人策略学习同样可从理解、规划与连续未来表示学习的协同优势中受益。基于此洞见，我们提出UniCoD，该方法通过在超过100万条互联网规模的教学操作视频上进行预训练，获得对高维视觉特征进行动态建模的能力。随后，UniCoD在机器人本体收集的数据上进行微调，从而学习从预测性表征到动作标记的映射。大量实验表明，我们的方法在模拟环境和真实世界分布外任务中分别以9%和12%的优势持续超越基线方法。