RoboTALES: 通过任务对齐的模拟未来学习推理引导的机器人策略

摘要

预训练视频生成模型为视觉运动控制提供了有前景的基础框架，但其生成的未来状态常偏离任务意图，且无法可靠地实现动作条件化。因此，这类模型难以用于规划或策略提取。为解决这些局限，我们提出RoboTALES——一种单阶段框架，通过学习任务对齐的模拟未来状态训练机器人策略。该方法包含两项关键创新：(1) 基于层级LLM的规划器，将复杂任务分解为子目标序列以引导模型想象；(2) 基于VLM的评判器，通过评估这些"想象"的未来状态并利用奖励反馈，使模型内部表征始终聚焦于目标。通过将视频生成器锚定于抽象推理，我们实现了时间一致的推演与更连贯的动作。在RoboCasa和LIBERO10的多样化操作任务上评估显示，我们的方法始终优于现有方法，尤其在长时域任务中表现突出。代码与模型已开源：https://github.com/hananshafi/RoboTALES。

English

Pretrained video generative models are promising backbones for visuomotor control, but their imagined futures often drift from task intent and are not reliably action-conditional. As a result, these models can be difficult to use for planning or policy extraction. To address these limitations, we propose RoboTALES, a single-stage framework that learns task-aligned simulated futures and uses them to train robot policies. Our approach introduces two key innovations: (1) a hierarchical LLM-based planner that breaks complex tasks into a sequence of subgoals to guide the model's imagination; and (2) a VLM-based critic that evaluates these ``imagined'' futures and uses reward-based feedback to keep the model's internal representations focused on the goal. By anchoring the video generator in abstract reasoning, we produce temporally consistent rollouts and more coherent actions. We evaluate RoboTALES on diverse manipulation tasks from RoboCasa and LIBERO10, and show that our method consistently outperforms existing methods, especially in long-horizon tasks. Our code and models are publicly available at https://github.com/hananshafi/RoboTALES.