Thinker: A vision-language foundation model for embodied intelligence
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917230827012096 |
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| author | Pan, Baiyu Luo, Daqin Yang, Junpeng Wang, Jiyuan Zhang, Yixuan Shi, Hailin Jiao, Jichao |
| author_facet | Pan, Baiyu Luo, Daqin Yang, Junpeng Wang, Jiyuan Zhang, Yixuan Shi, Hailin Jiao, Jichao |
| contents | When large vision-language models are applied to the field of robotics, they encounter problems that are simple for humans yet error-prone for models. Such issues include confusion between third-person and first-person perspectives and a tendency to overlook information in video endings during temporal reasoning. To address these challenges, we propose Thinker, a large vision-language foundation model designed for embodied intelligence. We tackle the aforementioned issues from two perspectives. Firstly, we construct a large-scale dataset tailored for robotic perception and reasoning, encompassing ego-view videos, visual grounding, spatial understanding, and chain-of-thought data. Secondly, we introduce a simple yet effective approach that substantially enhances the model's capacity for video comprehension by jointly incorporating key frames and full video sequences as inputs. Our model achieves state-of-the-art results on two of the most commonly used benchmark datasets in the field of task planning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_21199 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Thinker: A vision-language foundation model for embodied intelligence Pan, Baiyu Luo, Daqin Yang, Junpeng Wang, Jiyuan Zhang, Yixuan Shi, Hailin Jiao, Jichao Computer Vision and Pattern Recognition Artificial Intelligence When large vision-language models are applied to the field of robotics, they encounter problems that are simple for humans yet error-prone for models. Such issues include confusion between third-person and first-person perspectives and a tendency to overlook information in video endings during temporal reasoning. To address these challenges, we propose Thinker, a large vision-language foundation model designed for embodied intelligence. We tackle the aforementioned issues from two perspectives. Firstly, we construct a large-scale dataset tailored for robotic perception and reasoning, encompassing ego-view videos, visual grounding, spatial understanding, and chain-of-thought data. Secondly, we introduce a simple yet effective approach that substantially enhances the model's capacity for video comprehension by jointly incorporating key frames and full video sequences as inputs. Our model achieves state-of-the-art results on two of the most commonly used benchmark datasets in the field of task planning. |
| title | Thinker: A vision-language foundation model for embodied intelligence |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2601.21199 |