目标基准测试：世界模型能否实现基于语义目标的无地图路径规划？

摘要

尽管当前的世界模型能够生成高度逼真的视频，但其在机器人路径规划方面的能力仍不明确且缺乏量化评估。我们推出Target-Bench——首个专门用于评估世界模型在真实环境中实现无地图语义目标路径规划的基准测试。该基准提供450段机器人实地采集的视频序列，涵盖45个语义类别，并配有基于SLAM技术的真实轨迹数据。我们的评估流程通过从生成视频中还原相机运动，采用五项互补指标来衡量规划性能，这些指标可量化模型的目标抵达能力、轨迹精度和方向一致性。我们对包括Sora 2、Veo 3.1及Wan系列在内的前沿模型进行评估，发现最佳现成模型（Wan2.2-Flash）仅获得0.299的综合评分，揭示了当前世界模型在机器人规划任务中的显著局限。实验表明，仅使用本数据集中的325个场景对开源50亿参数模型进行微调，即可获得0.345的综合评分——较其基础版本（0.066）提升超400%，并优于最佳现成模型15%。我们将开源相关代码与数据集。

English

While recent world models generate highly realistic videos, their ability to perform robot path planning remains unclear and unquantified. We introduce Target-Bench, the first benchmark specifically designed to evaluate world models on mapless path planning toward semantic targets in real-world environments. Target-Bench provides 450 robot-collected video sequences spanning 45 semantic categories with SLAM-based ground truth trajectories. Our evaluation pipeline recovers camera motion from generated videos and measures planning performance using five complementary metrics that quantify target-reaching capability, trajectory accuracy, and directional consistency. We evaluate state-of-the-art models including Sora 2, Veo 3.1, and the Wan series. The best off-the-shelf model (Wan2.2-Flash) achieves only 0.299 overall score, revealing significant limitations in current world models for robotic planning tasks. We show that fine-tuning an open-source 5B-parameter model on only 325 scenarios from our dataset achieves 0.345 overall score -- an improvement of more than 400% over its base version (0.066) and 15% higher than the best off-the-shelf model. We will open-source the code and dataset.

目标基准测试：世界模型能否实现基于语义目标的无地图路径规划？

Target-Bench: Can World Models Achieve Mapless Path Planning with Semantic Targets?

摘要

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