Perception

Feature Extraction

Feature extraction is the process of transforming raw sensor data, such as images or point clouds, into compact representations that capture information useful for downstream tasks. Classical pipelines used hand-engineered descriptors like SIFT, ORB, and FPFH, while modern systems learn features with convolutional networks or vision transformers, often pretrained at scale (for example DINOv2 or CLIP encoders) and reused across detection, pose estimation, and policy learning.

Why it matters for physical AI

Robot policies are only as good as the representations they consume; strong pretrained visual features are a key ingredient that lets manipulation and navigation models generalize from limited robot data.