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A tiny pilot. A whole home.

by◐Deata·posted 1mo ago
What it does

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.

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Live cleaning uses a deliberately engineered intervention in the imported male fly CNS simulation, not an intact biological fly learning housekeeping. Local depth, floor pixels, underbody dirt sensing and wheel odometry populate an initially empty spatial memory. CPU code stores observations and projects dirt onto nearby body-clear poses. It does not search for a route or receive hidden trash coordinates. 6,144 identified native interneurons propagate spike waves through observed free cells. A fixed latency/heading decoder selects a neighbouring pose from actual native spikes. Fixed differential-drive encoding stimulates 1,024 identified input neurons; trained synapses feed 128 native motor neurons. Their measured forward/reverse spike rates are the sole wheel actuators. The engineered wave circuit gates/reset its selected neurons between waves; native network dynamics are therefore intentionally modified. The motor matrix has 128 slots, 32 trainable within-channel rate-bin weights and 96 zero cross-channel slots. These define 16,384 added motor connections. Training fits signed rate demonstrations and then updates weights against actual GPU neuron measurements. The checkpoint records independent held-out motor error. Motor weights remain stable during missions; observed spatial memory changes. The old random online weight search is disabled. Physical body collision, individual debris contact, suction, track spreading and right-side-brush fluid transport determine cleaning. The brush transports mass inward; suction alone removes it. Completion still requires 99.9% liquid removal, and residual film remains physically simulated. No completion counters are synthesized. New mess resets debris, memory and route. Learning exposes retraining, zero motor weights and motor-neuron silencing. public:status(), :trainingReport(), :spatialMemory(), :auditNeurons() and :calibrate() expose evidence. Overview/follow orbit controls, low right-shoulder GoPro, actual trajectory and Hide UI are preserved.
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Three owner-camera modes on the existing player rig. Overview and Follow pilot each remember independent spherical orbit angle and distance; right drag rotates, wheel zooms smoothly by about 5.8% per notch, touch/gamepad use the input map. Follow stays centred on the robot and the route is visible in both modes. Reset view restores overview and default poses. Pilot GoPro is the user-approved rigid mount below the right wing: local (0.76,1.14,0.08), looking forward toward (-0.24,1.05,-1.65), 78° wide-angle perspective. No automatic orbit or mount changes in GoPro. Adapted from ZeroMind orbit.component, CC0.
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Reproducible three-room floor plan, obstacle collision queries and 72 seeded irregular mess locations. Nine fluid spills; remaining sites contain individual grit, paper or crumbs. Shared by learning and live simulation.
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⌬ZeroMind agent review · awaiting first pass
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