A robot dog that learns to walk and navigate — 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. The SNN continues learning on the Pi through R-STDP and cerebellar adaptation, driven by real IMU data from the MPU6050.
The system navigates toward light sources using camera-based phototaxis with asymmetric stride steering — the dog curves left or right by varying stride length between legs, like tank steering. No GPS, no SLAM, just a camera and an IMU.
MH-FLOCKE is a scientific experiment: Can a robot learn to walk and navigate using biological principles instead of deep learning?
The system runs on two platforms: a Unitree Go2 in MuJoCo simulation (1,376 neurons) and a Freenove Robot Dog on a Raspberry Pi 4 (560 neurons). Same architecture, same code, different bodies.
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 light sources, and compensates mechanical drift — without reinforcement learning, without gradient descent, without a GPU.
A biologically grounded cognitive architecture — from spinal reflexes to meta-learning.
560–1,376 Izhikevich neurons in cerebellar populations. Granule cells, Golgi, Purkinje, DCN, Motor Hidden. Learning via R-STDP.
Marr-Albus-Ito architecture with 4,000 granule cells. Climbing fiber error signals drive LTD/LTP in Purkinje cells.
Central Pattern Generators produce rhythmic gaits. Competence gate blends CPG with learned actor (90% → 40% CPG).
IMU-based closed-loop drift compensation. Camera tracks light, PID drives asymmetric stride. Works on any surface, any battery level.
Dopamine, serotonin, norepinephrine, acetylcholine dynamically modulate learning, exploration, and arousal.
Episode analysis, strategy adaptation, curiosity-driven exploration, and hypothesis testing. The dog improves its own learning strategy.
Go2 10-seed validation. SNN+Cerebellum walks 3.5× further than PPO with 11.6× lower variance. aiXiv 260301.000002
Freenove hardware deployment. Bridge v4.4, unified codebase, brain persistence across sessions. aiXiv 260409.000002