Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact with the world. RGB cameras record 2D images with rich semantic information while missing precise spatial information. On the other side, depth cameras offer critical 3D geometry data but capture limited semantics. Therefore, integrating both modalities is crucial for learning representations for robotic perception and control. However, current research predominantly focuses on only one of these modalities, neglecting the benefits of incorporating both. To this end, we present $\textbf{Semantic-Geometric Representation} (\textbf{SGR})$, a universal perception module for robotics that leverages the rich semantic information of large-scale pre-trained 2D models and inherits the merits of 3D spatial reasoning. Our experiments demonstrate that SGR empowers the agent to successfully complete a diverse range of simulated and real-world robotic manipulation tasks, outperforming state-of-the-art methods significantly in both single-task and multi-task settings. Furthermore, SGR possesses the capability to generalize to novel semantic attributes, setting it apart from the other methods. Project website: https://semantic-geometric-representation.github.io.

翻译：机器人高度依赖传感器（尤其是RGB和深度相机）来感知并与世界交互。RGB相机记录的二维图像包含丰富的语义信息，但缺乏精确的空间信息；而深度相机提供关键的三维几何数据，但捕获的语义有限。因此，融合这两种模态对于学习机器人感知与控制的表示至关重要。然而，当前研究主要聚焦于单一模态，忽视了结合两者的优势。为此，我们提出$\textbf{语义-几何表示}（\textbf{SGR}）$，一种用于机器人的通用感知模块，该模块利用大规模预训练二维模型的丰富语义信息，并继承三维空间推理的优点。我们的实验表明，SGR使智能体能够成功完成一系列模拟和真实环境中的机器人操作任务，在单任务和多任务设置下均显著优于最先进方法。此外，SGR具备泛化至新型语义属性的能力，这使其区别于其他方法。项目网站：https://semantic-geometric-representation.github.io。