RoboTALES：通过任务对齐的模拟未来学习推理引导的机器人策略
RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
AI总结：
针对预训练视频生成模型用于机器人策略规划的局限，提出RoboTALES框架，通过基于分层语言模型的规划器和基于视觉语言模型的评论家两大创新，学习任务对齐的模拟未来以训练机器人策略，在多样操纵任务中表现优于现有方法。
摘要：
预训练视频生成模型是视觉运动控制的有前景的主干，但它们想象的未来常偏离任务意图且不可靠地以动作条件为依据，难以用于规划或策略提取。为解决这些局限，我们提出RoboTALES，一个单阶段框架，学习任务对齐的模拟未来并用于训练机器人策略。该方法有两个关键创新：基于分层语言模型的规划器将复杂任务分解为子目标序列引导模型想象；基于视觉语言模型的评论家评估这些“想象”的未来并使用基于奖励的反馈使模型内部表征聚焦目标。通过将视频生成器锚定在抽象推理中，我们产生时间上一致的展开和更连贯的动作。我们在来自RoboCasa和LIBERO10的各种操纵任务上评估RoboTALES，表明我们的方法始终优于现有方法，特别是在长视野任务中。我们的代码和模型可在指定网址公开获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
