Target-Bench：動画世界モデルは意味的ターゲットへの地図なし経路計画を達成できるか？
Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets?
動画世界モデルの意味的推論・空間推定・計画能力を評価する初のベンチマークTarget-Benchを提案し、既存モデルの性能不足と実ロボットデータでの微調整による改善を示した。
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？
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著者: Dingrui Wang, Zhihao Liang, Hongyuan Ye, Zhexiao Sun, Zhaowei Lu, Yuchen Zhang, Yuyu Zhao, Yuan Gao, Marvin Seegert, Finn Schäfer, Haotong Qin, Wei Li, Luigi Palmieri, Felix Jahncke, Mattia Piccinini, Johannes Betz
分類: cs.CV, cs.RO
原文アブストラクト
While recent video world models can generate highly realistic videos, their ability to perform semantic reasoning and planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark that enables comprehensive evaluation of video world models' semantic reasoning, spatial estimation, and planning capabilities. Target-Bench provides 450 robot-collected scenarios spanning 47 semantic categories, with SLAM-based trajectories serving as motion tendency references. Our benchmark reconstructs motion from generated videos with a metric scale recovery mechanism, enabling the evaluation of planning performance with five complementary metrics that focus on target-approaching capability and directional consistency. Our evaluation result shows that the best off-the-shelf model achieves only a 0.341 overall score, revealing a significant gap between realistic visual generation and semantic reasoning in current video world models. Furthermore, we demonstrate that fine-tuning process on a relatively small real-world robot dataset can significantly improve task-level planning performance.
