Used Vector robot with GPT-6 Astra learns to avoid furniture through reinforcement
The author stopped hand-tuning movement and instead reinforced behaviors that reach targets without collisions while penalizing ones that get stuck or crash, letting the robot with a reconstructed fly-brain simulation learn to avoid chairs and table legs.
UPDATE: Flyctor is starting to evolve I stopped manually tuning the way it moves. Instead, I gave it a simple rule: Every time it reaches a target without hitting anything, the behavior that got it there is reinforced. Every time it gets stuck, crashes into furniture, or drives in circles, that behavior loses weight. Then I let it run. Flyctor still has the same body: - a used Vector robot from eBay - a camera - GPT-6 Astra turning video into simple sensory signals - a reconstructed fly-brain simulation choosing the next movement But now the connection between "what it sees" and "what it does" is no longer fixed. It changes after every attempt. At first, Flyctor behaved exactly like you would expect from a digital fly brain inside a cheap pet robot. It saw an open path. Turned too early. Corrected too hard. Hit a chair. Repeated the same mistake. Then something changed. It began slowing down before narrow gaps. It stopped making sharp turns near table legs. It started taking longer routes around obstacles instead of repeatedly trying to force its way through them. Nobody wrote those behaviors. Nobody told it what a chair is. The system just learned that some actions lead to movement, and some actions lead to being stuck. I know this is not biological evolution. But watching a tiny robot change its behavior because the old version of itself kept failing feels uncomfortably close. The first Flyctor was a simulation inside a robot body. This one is becoming a slightly better version of itself every time it gets lost.