  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.
