RoboTALES: タスク整合的なシミュレート未来による推論誘導型ロボットポリシーの学習
RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
事前学習済みビデオ生成モデルをロボット制御に活用する際、想像した未来がタスク意図からずれたり、行動条件付けが不確実になる問題を解決するため、階層的LLMプランナーとVLM批評家を用いてタスク整合的な未来を生成し、ロボットポリシーを学習する単一フレームワークを提案した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Hanan Gani, Tejal Kulkarni, Madhoolika Chodavarapu, Nicklas Hansen, Manmohan Chandraker
分類: cs.RO
原文アブストラクト
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.
