Small Brain. Big Clean.

A tiny pilot. A whole home.

Three furnished cutaway rooms with a fly-driven robotic vacuum, live neural render texture, pilot camera, individual debris and deforming coffee, paint and detergent spills. Uses the published fruitFly package, path_grit material and set_floorboards material.

Learning is an added goal-conditioned Q controller, trained with 180,000 exploratory transitions in this fixed house. The biological connectome weights stay fixed. Desired wheel commands stimulate left/right motor neurons; measured GPU spiking rates pass through a calibrated decoder into differential-drive motion. No authored cleaning route or pathfinder is used. Scene coordinates and remaining mess are available to the controller; this is not a vision-only or sim-to-real robot system.

Controls: Pause, New dust (reproducible layout), Silence/Restore motor neurons, Train again, Untrained baseline, room preference, Apartment and Follow fly + joystick.

Grid validation: 72/72 sites from each of three starts, including three visits per spill; random-action baselines 24, 45, 46 of 72 within 1,800 steps. These are training-house simulation results, not a claim of generalization. Liquid contact regression: remaining mass .64, .28, 0 after three passes; staying on a spill cannot clean it in one pass. Live visuals use the actual cleaning state and actual neuronal activity.