Building a Physical AI that Plays Connect 4

Aarnav Shah describes building a physical Connect 4-playing robot that combines local computer vision, a classical game engine, robotic hardware, and AI-generated voice commentary. A locally run RF-DETR model identifies red pieces, yellow pieces, the board, and empty cells, while homography converts detections into a 7-by-6 game grid and requires five consistent frames before accepting a move. Tactical decisions are made exclusively by a bitboard-based negamax engine that searches eight moves ahead in roughly 12 milliseconds and is tested against forced wins and defensive situations, while Gemini Flash and ElevenLabs generate and voice contextual trash talk without influencing gameplay. The $200 HiWonder MaxArm uses a suction nozzle to retrieve pieces, and a custom cardboard funnel mounted above the board reorients horizontally held chips and improves placement tolerance despite mechanical backlash. The project’s repository includes setup instructions, local vision and game-engine components, optional API-driven speech features, and calibration guidance emphasizing stable hardware, manually measured feeder coordinates, and board-specific camera mapping.

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