Object Recognition

Object recognition systems use computer vision and machine learning to identify objects in camera images. Classical approaches extract hand-crafted features (SIFT, SURF) and match against databases. Modern deep learning methods use convolutional neural networks (CNNs) trained on millions of labeled images. The network learns hierarchical features - edges, textures, parts, whole objects. During inference, the robot captures images, preprocesses them, and feeds them to the network which outputs class probabilities and detection bounding boxes. Instance recognition identifies specific objects (my coffee mug), while category recognition identifies object types (any mug). 3D object recognition combines RGB images with depth data for pose estimation. Confidence scores indicate recognition certainty.

Object recognition enables household robots to locate requested items - finding specific tools, ingredients, or personal belongings. Manipulation tasks identify target objects for grasping. Assembly robots recognize parts and verify correct component selection. Quality inspection identifies defects or wrong parts. Sorting systems classify items for appropriate handling. Navigation systems identify doors, stairs, and obstacles. Table clearing identifies dishes versus non-dish objects. Retail robots recognize products for inventory management. Understanding scenes requires recognizing multiple objects and their relationships.