The Freenove robot dog has a problem. It drifts to the right. Every unit is different — servo tolerances, weight distribution, surface friction — and no amount of calibration fixes it permanently. The drift changes with battery level, temperature, and floor surface.

For weeks, I tried to fix this with Z-offset steering — shifting all four feet sideways to create a turning moment. It seemed logical. It was useless. A 45-second hardware test proved it: ±5mm of Z-offset produces less than 5 degrees of effect against 70 degrees of mechanical drift. One measurement killed weeks of assumptions.

Tank Steering

The replacement is asymmetric stride — differential hip amplitude between left and right legs. Left legs take a longer step, the dog curves right. Like a tank. Biology does this through the reticulospinal tract, which modulates stride length independently per side.

Hardware test: drift reduced from 70 degrees to 8.5 degrees. Three times more effective than Z-offset, and it works on any surface because the IMU provides closed-loop feedback.

The PID controller reads the actual heading from the MPU6050 IMU, compares it to the target heading from the camera (where the light is), and drives the stride asymmetry. No calibration, no per-robot tuning. The I-term accumulates over time to eliminate steady-state offset — exactly what the cerebellum does biologically through long-term depression.

Eight Bugs in One Session

The steering replacement exposed eight bugs that had been hiding in the system:

The steering signal was computed correctly but silently dropped in compute_tendon() — wrong code path, one line fix. The log was showing a proxy value instead of the actual steering — the controller had been working the whole time, but the display said zero. The competence gate required walking speed above 0.03 m/s, but with drift, all locomotion energy went to correction instead of forward progress. The baby never grew up. MuJoCo yaw convention is inverted versus hardware — one minus sign. A threshold prevented target updates when the dog was roughly aimed at the light. The PD controller initialized its target to zero instead of the current heading.

Each one of these individually prevented the system from working. Finding them required switching between simulation and real hardware, comparing signs and values, and measuring instead of guessing.

The Dog Approaches the Light

After fixing all eight bugs and tuning the PID on hardware (Kp=0.05, Ki=0.01, Kd=0.015), the Freenove robot approaches a light source from 0.52 meters to 0.17 meters in 60 seconds. Not perfect tracking — the steering saturates near the end — but genuine, IMU-corrected, drift-compensated navigation on a 100-euro robot kit.

In simulation with measured hardware drift injected: 50,000 steps, zero falls, three light targets found, actor competence 1.0.

Meta-Learning Loop

This release also introduces the complete autonomous meta-learning loop — four modules that form a closed self-improvement cycle:

EpisodeAnalyzer compares successful versus unsuccessful navigation events and identifies what makes the dog successful. Which context variables (gait quality, heading error, velocity, steering offset) correlate with finding the light?

StrategyAdapter converts those insights into parameter adjustments — modifying run/tumble duration, PID gains, and exploration bias.

CuriosityExplorer uses world model prediction error to drive exploration. High prediction error means unfamiliar territory — explore more. Low prediction error means familiar ground — exploit what works.

HypothesisGenerator creates testable motor hypotheses from insights that can be tested autonomously through the existing Directed Learning module.

The loop runs but has not generated insights yet — the dog is too successful in the current scenario (100% success rate, no failures to learn from). Harder scenarios and longer runs will activate it.

Hardware Drift Simulation

The drift profile injected into the simulator has been updated. The previous measurement (-0.4 deg/s) was taken during a stationary test. Under walking load, the actual drift is 1.5 to 2.0 deg/s — servo asymmetry amplifies under dynamic conditions. The updated profile makes simulation training more realistic.

Every Freenove unit has different drift characteristics. MH-FLOCKE handles this automatically through the PID controller. No manual calibration needed.

What Comes Next

Hardware video with the new PID steering. Longer simulation runs to activate the meta-learning loop. A potential third robot platform (Petoi Bittle X V2) to prove the architecture is body-agnostic.

The code is on GitHub (Apache 2.0). Updated documentation at mhflocke.com/docs.

v0.5.1 — Full changelog

A 560-Neuron Spiking Network Steers a Quadruped Toward Light

For the first time, MH-FLOCKE’s robot dog actively navigates toward a light source in simulation — driven by hardwired reflexes and learned neural adaptations. No external reward signal. No reinforcement learning. Just body signals.

What You See in the Video

A Freenove Robot Dog (€100, Raspberry Pi, 12 servos) walks across a flat surface in MuJoCo simulation. A light source (yellow dot on the mini-map) is placed ahead and to the side. The dog detects the light gradient and steers toward it using a VOR (Vestibulo-Ocular Response) reflex — a hardwired brainstem circuit that turns the body toward a visual target.

The mini-map in the bottom-left corner shows the robot’s trail (green line) and the light waypoint (yellow glow). You can see the dog arcing toward the light rather than walking straight. This arc is not a software limitation — it reflects the physical turning radius of the Freenove’s CPG gait, which produces roughly 12% steering asymmetry between left and right legs.

Hardwired vs. Learned: The Biological Design

MH-FLOCKE follows the same principle as biological motor development. A newborn puppy doesn’t learn to walk from scratch — its spinal cord has CPG circuits that produce rhythmic leg movements from birth. The cerebellum calibrates these movements through experience. The brainstem provides reflexes like the VOR. Learning refines what reflexes provide.

Hardwired components (present from “birth”):

• CPG (Central Pattern Generator) — Mathematical oscillator producing rhythmic gait. The SNN does not generate the gait pattern; CPG provides the baseline.
• VOR (Vestibulo-Ocular Response) — Reflexive steering toward the light target. Hardwired, like in a real animal’s superior colliculus.
• Run-and-Tumble — A bacterial-inspired navigation state machine (Berg & Brown, 1972). Alternates between running straight and turning (tumbling) when the gradient changes.
• Spinal reflexes — Righting reflex, cross-extension reflex, terrain compensation.

Learned through training (emerges from experience):

• SNN weights (R-STDP) — Reward-modulated spike-timing-dependent plasticity adapts 560 neuron connections based on intrinsic reward (vestibular comfort, prediction error, curiosity).
• Cerebellar correction (Marr-Albus-Ito) — The cerebellum learns forward-model corrections. Correction magnitude grows from 0.0006 to 0.034 over training — the strongest cerebellar signal ever measured in MH-FLOCKE.
• CPG-to-SNN handoff — The CPG starts at 90% control and fades to ~45% as the SNN proves it can maintain stable locomotion. The SNN earns control through competence, not through a timer.

The Numbers

• 33,000 steps, 9.7 minutes training time (57 sps on CPU)
• 0 falls, perfect upright streak
• 2 light targets reached (sf:2) through active VOR-guided steering
• VOR signal up to +0.54 — strong, sustained steering toward the light
• 4 Run-and-Tumble events — the navigation state machine triggered naturally
• Cerebellar correction: 0.008 — real Marr-Albus-Ito learning

Why the Dog Arcs Around the Light

You’ll notice the dog doesn’t walk straight to the light — it takes a wide arc. This is not a bug. The Freenove’s CPG produces approximately 12% amplitude asymmetry between left and right legs when steering. This gives the robot a turning radius of roughly 5 meters. The VOR reflex fires correctly, and the CPG responds — but the body can only turn as fast as the legs allow.

This is exactly what happens with real quadrupeds. A horse can’t make the same tight turns as a cat. The steering intention is there; the biomechanics set the limit.

Performance Breakthrough: 6× Speedup

This session also resolved a critical performance bug. Step-time was growing from 20ms to 800ms over 100k steps — making long training runs impossible. The root cause: an O(N²) clustering operation in the Synaptogenesis module that processed 5,000 accumulated experience patterns without clearing the buffer.

The fix (buffer.clear() after consolidation + max_size reduction) brought step-time back to a stable 18ms across 100k steps. Training speed went from 7 sps to 54 sps — a 6× improvement that makes all future development viable.

What’s Next

• Hardware phototaxis — The same VOR steering with a real camera (cv2) on the Freenove, following a flashlight on the floor.
• Autonomous loop — Instead of pre-placed waypoints, the dog chooses its own targets based on curiosity, exploration drive, and episodic memory. All the modules exist; they need a conductor.
• Paper 2 — Sim-to-real transfer + phototaxis results for Frontiers in Neurorobotics or CoRL workshop.

MH-FLOCKE is an open-source project by Marc Hesse — independent researcher, Potsdam, Germany. Named after Flocke, my late dog.

Code: github.com/MarcHesse/mhflocke (Apache 2.0)
Paper: aiXiv Preprint