TruState Robotics · Education
Computer Vision Lab
Our robots navigate real homes by turning camera pixels into decisions. In this lab, you'll learn to do the same: teach a 2D cleaning robot to see across six levels — starting with what a pixel even is, ending with pattern recognition. Snap blocks together if you're new, or write real JavaScript if you're ready.
Booting the robot's camera…
From this game to real computer vision
Every block you snapped together exists as a battle-tested function in OpenCV, the library behind most real vision systems — including the ones we prototype for TruState robots. Here's your translation table:
1 · Pixels & Brightnesscv2.imread / cv2.cvtColor — every image is an array of numbers
2 · Thresholdingcv2.threshold, Otsu's method — the simplest segmentation
3 · Color ChannelsHSV color space + cv2.inRange — color-based detection
4 · Blur & Convolutioncv2.GaussianBlur / medianBlur — and the core operation of CNNs
5 · Blob Detectioncv2.connectedComponentsWithStats / findContours — pixels become objects
6 · Template Matchingcv2.matchTemplate — the ancestor of modern object recognition
Next steps: install Python and OpenCV (pip install opencv-python), load a photo of your own floor, and rebuild Level 2 on a real image. Then come tell us how it went — we're always looking for people who can make robots see.