正文：
近年来，人工智能领域的研究不断取得突破，特别是在对空间智能的理解和应用方面。李飞飞教授提出的空间智能概念，为人工智能如何更好地理解和处理三维空间信息提供了新的视角。在此背景下，上海交通大学、智源人工智能研究院、北京大学等多所高校的研究者提出了一种名为SpatialBot的空间大模型，旨在帮助多模态大模型在通用场景和具身场景下更好地理解深度和空间。
SpatialBot模型的提出，是对李飞飞教授空间智能概念的有力回应。该模型通过引入RGB-Depth理解技术，能够准确获取机械爪和目标物体的深度值，从而实现对空间概念的深入理解。在具身智能的Pick and Place任务中，SpatialBot能够判断机械爪是否碰到了目标物体，并据此决定是否合爪抓取。
为了实现这一目标，研究者们设计了三个层次的SpatialQA数据集，以逐步引导模型理解深度图和深度信息。在低级别，模型能够直接从深度图中获取信息；在中级别，模型能够将深度图与RGB图像对齐；在高级别，模型能够在理解深度图的基础上，完成包括空间位置关系、物体大小、物体接触与否等深度相关任务。
此外，研究者们还公布了SpatialBench榜单，通过精心设计和标注的QA测试，评估模型在深度理解能力上的表现。SpatialBot在榜单上的表现与GPT-4o接近，显示了其在空间智能领域的强大潜力。
SpatialBot模型的提出，不仅为人工智能在具身场景中的应用提供了新的解决方案，也为未来人工智能在复杂环境下的自主导航和交互提供了可能。随着研究的深入，我们有理由相信，SpatialBot和其他类似模型将在未来的人工智能发展中扮演重要角色。
英语如下：
Title: “SpatialBot: A Space Large Model Breaks New Ground in Deep Understanding of Embodied Intelligence”
Keywords: Space Large Model, Deep Understanding, SpatialBot
News Content:
Recent breakthroughs in the field of artificial intelligence research, particularly in the understanding and application of spatial intelligence, have seen significant progress. Professor Li Fei-Fei’s concept of spatial intelligence offers a new perspective on how artificial intelligence can better understand and process three-dimensional spatial information. Against this backdrop, researchers from institutions such as Shanghai Jiao Tong University, the Institute of AI Research at Zhiyuan, Peking University, and others have proposed a space large model named SpatialBot, aiming to enhance the understanding of depth and space for multimodal large models in both general and embodied scenarios.
The introduction of the SpatialBot model represents a powerful response to Professor Li Fei-Fei’s concept of spatial intelligence. The model incorporates RGB-Depth understanding technology to accurately acquire depth values for robotic grippers and target objects, thus achieving a deep understanding of spatial concepts. In embodied intelligence tasks such as Pick and Place, SpatialBot can determine whether a robotic gripper has touched a target object and decide accordingly whether to close the gripper to grasp it.
To achieve this goal, researchers have designed three levels of SpatialQA datasets to gradually guide the model in understanding depth maps and depth information. At the basic level, the model can directly obtain information from depth maps; at the intermediate level, it can align depth maps with RGB images; at the advanced level, it can complete tasks related to spatial relationships, object sizes, and whether objects are in contact, based on its understanding of depth maps.
In addition, the researchers have released the SpatialBench leaderboard, which evaluates models’ performance in depth understanding through carefully designed and annotated QA tests. SpatialBot’s performance on the leaderboard is comparable to that of GPT-4, indicating its strong potential in the field of spatial intelligence.
The introduction of the SpatialBot model not only provides new solutions for the application of artificial intelligence in embodied scenarios but also opens up possibilities for autonomous navigation and interaction in complex environments in the future. As research deepens, we have reason to believe that SpatialBot and similar models will play a significant role in the future development of artificial intelligence.
【来源】https://www.jiqizhixin.com/articles/2024-08-07-2
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