Abstract
A humanoid robot system comprises vision sensors for capturing image data, a computing architecture with processing hardware and memory, and a bipedal spatial perception model. The model includes a feature extractor that extracts hierarchical feature maps from input images, a robot data module that detects robot parts, and a robot vector data module that calculates three-dimensional spatial position and orientation data for each detected robot part. The feature extractor uses a feature pyramid network generating multi-scale feature maps through bottom-up and top-down pathways with lateral connections. The robot vector data module predicts 2D-to-3D point correspondences and solves perspective-n-point problems to obtain final position and orientation vectors, enabling real-time robot self-awareness and closed-loop visual servoing for precise object interaction.