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          <h1 class="title is-1 publication-title">EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought</h1>
          <div class="is-size-5 publication-authors">
            <span class="author-block">
              Yao Mu, Qinglong Zhang, Mengkang Hu, Wenhai Wang, Mingyu Ding, Jun Jin, Bin Wang, Jifeng Dai, Yu Qiao, Ping Luo<sup>1</sup>
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        <h2 class="title is-3">Abstract</h2>
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          <p>
            Embodied AI represents a critical frontier in robotics, where the capacity to devise coherent action sequences for robots to accomplish specific tasks in physical environments is both essential and fraught with inherent challenges. Recently, there has been an increasing interest in developing large foundation models capable of generating embodied plans by processing natural language instructions and environmental observations. In this paper, we introduce EmbodiedGPT, a large-scale end-to-end multi-model foundation model for embodied AI.
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          <p>
             Our contributions can be categorized into three folds: First, we meticulously craft a large-scale embodied planning dataset, encompassing extensive videos and planning instructions derived from the Ego4D dataset, with scrupulous prompt design and quality assurance. Second, we advocate a cost-effective training method for end-to-end multimodal large models, generating a sequence of sub-goals in planning pursuant to observations by harnessing the potency of the "Chain of Thought." Specifically, we augment the 7B language model's capacity to produce high-quality planning by employing prefix adapters to train it on the EgoCOT dataset, circumventing overly divergent language model responses.
Lastly, we introduce a paradigm for extracting task-related features from LLM-generated planning queries, forming a closed loop between high-level planning and low-level control. Our comprehensive experiments substantiate that our model effectively bolsters the performance of embodied tasks, including Embodied Planning, Embodied Control, Visual Captioning, and Visual Q&A.
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              The whole framework is shown as follow:
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              <p>Start Frame</p>
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            Open the cabinet
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            <img src="./static/images/cabinet_3.gif"
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            Turn the light on
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            Open the microwave oven
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            Slide the door open
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            Assemble task
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            Place the bin
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            Hammer the nail
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            Open the drawer
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    <h2 class="title">BibTeX</h2>
    <pre><code>@article{mu2023embodiedgpt,
  title={Embodiedgpt: Vision-language pre-training via embodied chain of thought},
  author={Mu, Yao and Zhang, Qinglong and Hu, Mengkang and Wang, Wenhai and Ding, Mingyu and Jin, Jun and Wang, Bin and Dai, Jifeng and Qiao, Yu and Luo, Ping},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  pages={25081--25094},
  year={2023}
}</code></pre>
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