RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures

Pretrained video generative models are promising backbonesfor visuomotor control, but their imagined futures often drift from taskintent and are not reliably action-conditional. As a result, these mod-els can be difficult to use for planning or policy extraction. To addressthese limitations, we propose RoboTALES, a single-stage framework thatlearns task-aligned simulated futures and uses them to train robot poli-cies. Our approach introduces two key innovations: (1) a hierarchicalLLM-based planner that breaks complex tasks into a sequence of sub-goals to guide the model’s imagination; and (2) a VLM-based critic thatevaluates these “imagined” futures and uses reward-based feedback tokeep the model’s internal representations focused on the goal. By an-choring the video generator in abstract reasoning, we produce temporallyconsistent rollouts and more coherent actions. We evaluate RoboTALESon diverse manipulation tasks from RoboCasa and LIBERO10, and showthat our method consistently outperforms existing methods, especiallyin long-horizon tasks. Our code and models are publicly available athttps://github.com/hananshafi/RoboTALES.