ForeAct: 効率的な視覚的先読み計画でVLAを誘導する
ForeAct: Steering Your VLA with Efficient Visual Foresight Planning
将来の観測画像とサブタスク記述を生成してVLAの視覚入力に追加するだけで、アーキテクチャ変更なしに実世界タスクの成功率を大幅に向上させる計画モジュールを提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zhuoyang Zhang, Shang Yang, Qinghao Hu, Luke J. Huang, James Hou, Yufei Sun, Yao Lu, Song Han
分類: cs.RO, cs.AI
原文アブストラクト
Vision-Language-Action (VLA) models convert high-level language instructions into concrete, executable actions, a task that is especially challenging in open-world environments. We present Visual Foresight Planning (ForeAct), a general and efficient planner that guides a VLA step-by-step using imagined future observations and subtask descriptions. With an imagined future observation, the VLA can focus on visuo-motor inference rather than high-level semantic reasoning, leading to improved accuracy and generalization. Our planner comprises a highly efficient foresight image generation module that predicts a high-quality 640$\times$480 future observation from the current visual input and language instruction within only 0.33s on an H100 GPU, together with a vision-language model that reasons over the task and produces subtask descriptions for both the generator and the VLA. Importantly, state-of-the-art VLAs can integrate our planner seamlessly by simply augmenting their visual inputs, without any architectural modification. The foresight generator is pretrained on over 1 million multi-task, cross-embodiment episodes, enabling it to learn robust embodied dynamics. We evaluate our framework on a benchmark that consists of 11 diverse, multi-step real-world tasks. It achieves an average success rate of 87.4%, demonstrating a +40.9% absolute improvement over the $\pi_0$ baseline (46.5%) and a +30.3% absolute improvement over $\pi_0$ augmented with textual subtask guidance (57.1%).
