Snath Robotics: Multi-Stream Divergence Routing for Humanoid Robotics

This paper formalises the application of the V1–V6 cognitive routing contract 
to humanoid robotics, constituting the fourth domain instantiation of the 
Lár-JEPA architecture (UCR: doi:10.5281/zenodo.20278775, DAS: 
doi:10.5281/zenodo.20278781, AIA: doi:10.5281/zenodo.20419182).

The architecture maintains two structurally independent latent streams — visual 
appearance (z_vision) and proprioceptive physics (z_proprio, IMU + joints + 
tactile) — that are never fused. A mathematically frozen divergence router 
measures their total-variation distance D = ||softmax(z_A) − softmax(z_B)||₁ / √G 
and routes to one of four decisions: COMMIT_TRAJECTORY, TRIGGER_REPLAN, 
STRUCTURAL_IMPASSE, or DEFER. An overnight DMN consolidation cycle trains signed 
LoRA adapters from accumulated sensor-disagreement events and distributes them 
to the fleet, implementing a swarm learning mechanism in which a single failure 
event can improve the behaviour of every deployed unit.

Three contributions: (1) a formal mapping from the M1–M3 encoder independence 
invariants to sensor modalities; (2) robotics-specific temporal decay constants 
for three failure classes — environmental_transient (λ=0.50, ice/glare), 
sensor_drift (λ=0.20, calibration error), hardware_structural (λ=0.02, motor 
wear); (3) a precise statement of the boundary between routing safety — what the 
V1–V6 architecture guarantees by construction — and actuation safety — what 
requires integration with a physics or model-predictive control layer.

This is a position paper establishing the theoretical mapping and prior art. 
No empirical results are reported. Validation is deferred to AIA Experiment 3 
and a forthcoming hardware evaluation.

Reference implementation (Apache 2.0): https://github.com/snath-ai/snath-robotics