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arXiv · 2601.21199

Thinker: A vision-language foundation model for embodied intelligence

Baiyu Pan·Daqin Luo·Junpeng Yang·Jiyuan Wang·Yixuan Zhang·Hailin Shi·Jichao Jiao
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Abstract

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.

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Baiyu Pan, Daqin Luo, Junpeng Yang, Jiyuan Wang, Yixuan Zhang, Hailin Shi, Jichao Jiao. 2026-01-29. Thinker: A vision-language foundation model for embodied intelligence. https://arxiv.org/abs/2601.21199

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