Journal of Artificial Intelligence, Virtual Reality, and Human-Centered Computing
Wave-Based Neuromorphic Physical AI for Full-Body Humanoid Control: Distributed HBN Synapse Cavity Networks Mimicking Spinal, Peripheral, and Autonomic Neural Ar-chitecture
Abstract
Chur Chin
We present a unified physical AI architecture for full-body humanoid control based on distributed hexagonal boron nitride (hBN) synaptic cavities interconnected by serpentine waveguide networks. Inspired by the tripartite organisation of the biological nervous system - spinal cord, peripheral plexuses, and autonomic ganglia — the proposed framework maps wave interference directly onto motor computation without discrete digital processing. The central cavity (head) performs global wave interference analogous to spinal cord integration; modular peripheral cavities at each joint implement local reflex processing; and serpentine inter-cavity waveguides mimic the brachial plexus (C5–T1) and sciatic nerve (L4–S3) branching topology. The hBN memristor stack (8–18 atomic layers, CVD-grown, flexible PET substrate) provides attojoule-per-event synaptic switching and broadband tactile sensing up to the MHz regime via 23.5° twist- angle moiré phonon-polariton enhancement. A hierarchical control architecture integrating a high-level GR00T planning module with the real-time distributed wave system is formalised, including a wave-parameter mapping layer, Time- Sensitive Networking (TSN) communication protocol, and a Von Mises / Particle-Filter phase-tracking pipeline achieving sub-millisecond reflex latency. Simulation results confirm XOR-gate physical logic, Kepler-scaling echo memory (T∝ L1.2), and >98% classification accuracy at the optimal operating point (α = 0.88, Q = 104 ). This work establishes the theoretical and engineering foundation for a Physical AI humanoid whose nervous system is implemented entirely in the wave dynamics of a 2D-material heterostructure, with direct implications for energy-efficient robotics, neuromorphic prosthetics, and distributed embedded intelligence.