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| Auteurs principaux: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.17792 |
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| _version_ | 1866914474855759872 |
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| author | Wang, Dingrui Liang, Zhihao Ye, Hongyuan Sun, Zhexiao Lu, Zhaowei Zhang, Yuchen Zhao, Yuyu Gao, Yuan Seegert, Marvin Schäfer, Finn Qin, Haotong Li, Wei Palmieri, Luigi Jahncke, Felix Piccinini, Mattia Betz, Johannes |
| author_facet | Wang, Dingrui Liang, Zhihao Ye, Hongyuan Sun, Zhexiao Lu, Zhaowei Zhang, Yuchen Zhao, Yuyu Gao, Yuan Seegert, Marvin Schäfer, Finn Qin, Haotong Li, Wei Palmieri, Luigi Jahncke, Felix Piccinini, Mattia Betz, Johannes |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17792 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets? Wang, Dingrui Liang, Zhihao Ye, Hongyuan Sun, Zhexiao Lu, Zhaowei Zhang, Yuchen Zhao, Yuyu Gao, Yuan Seegert, Marvin Schäfer, Finn Qin, Haotong Li, Wei Palmieri, Luigi Jahncke, Felix Piccinini, Mattia Betz, Johannes Computer Vision and Pattern Recognition Robotics 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. |
| title | Target-Bench: Can Video World Models Achieve Mapless Path Planning with Semantic Targets? |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2511.17792 |