A robot dog that learns to walk, see, and play — not through programming, but through spiking neurons, a cerebellar forward model, and embodied experience.
MH-FLOCKE runs on real hardware. The Freenove Robot Dog Kit (~100€) with a Raspberry Pi 4 runs the same SNN and cerebellum code as the MuJoCo simulator — one codebase, two platforms.
A brain trained in simulation transfers directly to the real robot via a single file (brain.pt). The SNN continues learning on the Pi through R-STDP and cerebellar adaptation, driven by real IMU data from the MPU6050.
The live web dashboard shows all 6 cerebellar populations (MF, GrC, GoC, PkC, DCN, OUT) with real-time spike activity, servo angles, and competence gate — directly from the running SNN on the Pi.
MH-FLOCKE is a scientific experiment: Can an artificial system develop genuine understanding — not through programming, but through embodied experience?
The system receives a body (a Unitree Go2 quadruped in MuJoCo simulation), a world, and neurons. No calibration. No motor mapping. No hardcoded strategies. It must discover what it is, what it can do, and what the world is.
MH-FLOCKE implements spiking neural networks with R-STDP, a Marr-Albus-Ito cerebellar forward model, central pattern generators, and a Free Energy framework — all running simultaneously in a 15-step cognitive cycle.
The result: a quadruped that learns to walk within minutes, navigates toward interesting objects, and develops emergent behaviors like sniff → walk → trot → chase → alert — without any of these being programmed.
A biologically grounded cognitive architecture — from spinal reflexes to metacognition.
232–4,624 Izhikevich neurons in cerebellar architecture. Populations: mossy fibers, granule cells, Golgi, Purkinje, DCN. Learning via R-STDP.
Marr-Albus-Ito architecture predicts motor outcomes. Climbing fiber error signals drive LTD/LTP in Purkinje cells.
Central Pattern Generators produce rhythmic gaits. Spinal reflexes handle righting and cross-extension. PD controller bridges to Go2 torques.
Same codebase on Pi and simulator. Brain trained in MuJoCo runs on Freenove Robot Dog. Cerebellar learning continues on real hardware.
Dopamine, serotonin, norepinephrine, acetylcholine dynamically modulate learning, exploration, and arousal.
Sensory, motor, predictive, error, and memory modules compete for broadcast. Metacognition monitors consciousness level.
State-based distance signal: TPE = (ball_dist - 3.0) / 3.0. Clear gradient toward the ball.
When TPE exceeds 0.05, last 16 input neurons amplified by TPE × 0.5. Higher error = stronger sensory drive.
combined = 0.1 × reward + 0.9 × (−PE). Approaching reduces PE → positive reinforcement.
5 stages from (1.5m, 0°) to (3.0m, 34°). Advance when ball_dist_min < 0.5m. Two advances in 100k steps.