A few years into adult life, I ran out of people to play board games with on a Tuesday night. So I built one. Not an app, a physical robot that lives on the table, picks up the dice, moves the pieces, and reacts when it wins or loses.

This talk walks through the open-source stack I built on the LeRobot SO-101 arm: a perception module that turns an overhead camera into a structured game state, a per-game engine (rule-based for simple games, Stockfish for chess), an imitation-learned motor module trained from teleop, and an affect layer that gives the robot personality. It plays card games, Snakes and Ladders, and Ludo; chess is in progress.

I'll cover three things:
1. The engineering (what runs on-device, latency budget for vision plus arm control, which open models survived contact with a real board)
2. The affect layer (how to make the robot's emotions feel natural)
3. The failures (dropped pieces, misunderstood moves, detecting false moves)

You'll leave with a reference architecture, a soldering iron's worth of inspiration, and a sharper sense of why embodied affect is the under-explored layer in physical AI.