Egocentric Vision Language Planning
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arXiv
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| Format: | Preprint |
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2024
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| author | Fang, Zhirui Yang, Ming Zeng, Weishuai Li, Boyu Yue, Junpeng Ding, Ziluo Li, Xiu Lu, Zongqing |
| author_facet | Fang, Zhirui Yang, Ming Zeng, Weishuai Li, Boyu Yue, Junpeng Ding, Ziluo Li, Xiu Lu, Zongqing |
| contents | We explore leveraging large multi-modal models (LMMs) and text2image models to build a more general embodied agent. LMMs excel in planning long-horizon tasks over symbolic abstractions but struggle with grounding in the physical world, often failing to accurately identify object positions in images. A bridge is needed to connect LMMs to the physical world. The paper proposes a novel approach, egocentric vision language planning (EgoPlan), to handle long-horizon tasks from an egocentric perspective in varying household scenarios. This model leverages a diffusion model to simulate the fundamental dynamics between states and actions, integrating techniques like style transfer and optical flow to enhance generalization across different environmental dynamics. The LMM serves as a planner, breaking down instructions into sub-goals and selecting actions based on their alignment with these sub-goals, thus enabling more generalized and effective decision-making. Experiments show that EgoPlan improves long-horizon task success rates from the egocentric view compared to baselines across household scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_05802 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Egocentric Vision Language Planning Fang, Zhirui Yang, Ming Zeng, Weishuai Li, Boyu Yue, Junpeng Ding, Ziluo Li, Xiu Lu, Zongqing Computer Vision and Pattern Recognition We explore leveraging large multi-modal models (LMMs) and text2image models to build a more general embodied agent. LMMs excel in planning long-horizon tasks over symbolic abstractions but struggle with grounding in the physical world, often failing to accurately identify object positions in images. A bridge is needed to connect LMMs to the physical world. The paper proposes a novel approach, egocentric vision language planning (EgoPlan), to handle long-horizon tasks from an egocentric perspective in varying household scenarios. This model leverages a diffusion model to simulate the fundamental dynamics between states and actions, integrating techniques like style transfer and optical flow to enhance generalization across different environmental dynamics. The LMM serves as a planner, breaking down instructions into sub-goals and selecting actions based on their alignment with these sub-goals, thus enabling more generalized and effective decision-making. Experiments show that EgoPlan improves long-horizon task success rates from the egocentric view compared to baselines across household scenarios. |
| title | Egocentric Vision Language Planning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.05802 |