用视觉语言模型提升机器人对物体空间关系的理解能力。
Task-oriented Robotic Manipulation with Vision Language Models
- 将视觉场景转为树状结构表示空间关系，再由大语言模型推导任务所需布局。
- 在新构建的数据集上验证，显著提升机器人对空间关系的推理准确率。
- 适合需要复杂物体操作的智能机器人研发者参考。
视觉语言模型（VLMs）在机器人操作中发挥关键作用，使机器人能够理解物体及其环境的视觉属性，从而基于多模态理解执行操作。准确理解空间关系仍是重大挑战，但对有效操作至关重要。本文提出一种新框架，将VLMs与结构化空间推理流程结合，依据高层任务输入进行物体操作。该方法将视觉场景转化为树状结构表示，编码物体间的空间关系；随后由大语言模型（LLM）推断重构后的配置，确定物体在特定任务下的合理布局。为支持该框架，我们构建了一个新数据集，包含人工标注的空间关系描述及物体属性标注（如易碎性、质量、材质、透明度）。实验表明，该方法不仅提升了对视觉环境中物体间空间关系的理解能力，还增强了机器人与物体交互的有效性，显著改善了机器人操作中的空间推理表现。据我们所知，这是文献中首个此类方法，为机器人更高效地组织和利用周围物体提供了新解决方案。
原文摘要 · Abstract (English)
Vision Language Models (VLMs) play a crucial role in robotic manipulation by enabling robots to understand and interpret the visual properties of objects and their surroundings, allowing them to perform manipulation based on this multimodal understanding. Accurately understanding spatial relationships remains a non-trivial challenge, yet it is essential for effective robotic manipulation. In this work, we introduce a novel framework that integrates VLMs with a structured spatial reasoning pipeline to perform object manipulation based on high-level, task-oriented input. Our approach is the transformation of visual scenes into tree-structured representations that encode the spatial relations. These trees are subsequently processed by a Large Language Model (LLM) to infer restructured configurations that determine how these objects should be organised for a given high-level task. To support our framework, we also present a new dataset containing manually annotated captions that describe spatial relations among objects, along with object-level attribute annotations such as fragility, mass, material, and transparency. We demonstrate that our method not only improves the comprehension of spatial relationships among objects in the visual environment but also enables robots to interact with these objects more effectively. As a result, this approach significantly enhances spatial reasoning in robotic manipulation tasks. To our knowledge, this is the first method of its kind in the literature, offering a novel solution that allows robots to more efficiently organize and utilize objects in their surroundings.
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