Computational Simulation Model Blueprint (CSMB v1.0)

Dual-Plasticity Embodied Intelligence Simulator — research-grade, implementation-ready specification

1) Purpose and Scope

This blueprint defines a simulation framework to test the core claim of the AIAndroid concept:

Dual plasticity (synaptic learning + structural/topological reconfiguration) + embodiment produces faster adaptation, stronger transfer, and higher fault tolerance than static architectures.

The simulator is designed to evaluate capability and control in a measurable way before any hardware build.

2) High-Level Architecture

2.1 Simulation Layers

Environment Layer (E): physics + task generator + stochastic disturbances
Body Layer (B): kinematics/dynamics + sensors + actuators + damage model
Perception Layer (P): sensor fusion + feature extraction
Cognition Layer (C / NSC): policy, planning, world model interface
Structural Plasticity Layer (H / HNL): topology/routing reconfiguration engine
Governance Layer (G): constraints, safety gates, audit logging
Experiment Orchestrator (X): curricula, ablations, metrics, reproducibility

3) Formal System Definition

We model the AIAndroid as a controlled dynamical system with two coupled adaptation processes.

3.1 State Variables (Discrete Time)

Let time be $t = 0,1,2,\dots$ t=0,1,2,…

Environment state: $s_t \in \mathbb{R}^{n_s}$ st∈Rns
Body internal state: $b_t \in \mathbb{R}^{n_b}$ bt∈Rnb (pose, velocities, actuator temps, energy)
Observation (sensors): $o_t = \Omega(s_t, b_t) + \epsilon_t$ ot=Ω(st,bt)+ϵt
World model state (belief/memory): $m_t \in \mathbb{R}^{n_m}$ mt∈Rnm
Synaptic parameters (Phase I): $W_t$ Wt (weights)
Topology / routing parameters (Phase II): $T_t$ Tt (graph / adjacency / routing tables)
Policy parameters (derived): $\pi_{W_t,T_t}(a_t \mid z_t)$ πWt,Tt(at∣zt), where $z_t = \phi(o_{\le t}, m_t)$ zt=ϕ(o≤t,mt)

3.2 Dynamics

Environment + Body evolution

$(s_{t+1}, b_{t+1}) = F(s_t, b_t, a_t, \xi_t)$ (st+1,bt+1)=F(st,bt,at,ξt)

where $\xi_t$ ξt are exogenous disturbances (wind, friction change, object slip, adversarial noise, etc.).

World model update

$m_{t+1} = \Psi(m_t, o_t, a_t; W_t, T_t)$ mt+1=Ψ(mt,ot,at;Wt,Tt)

Action selection (cognition)

$a_t \sim \pi(\cdot \mid z_t; W_t, T_t)$ at∼π(⋅∣zt;Wt,Tt)

4) Dual Plasticity: Two Update Laws

4.1 Synaptic Plasticity (Learning)

Standard weight adaptation (online RL / predictive coding / supervised signals), expressed generally as: $W_{t+1} = W_t + \eta \, \Delta_W(\tau_t; W_t, T_t)$ Wt+1=Wt+ηΔW(τt;Wt,Tt)

where $\tau_t$ τt is a trajectory segment (observations, actions, rewards, constraints).

4.2 Structural Plasticity (Topological Reconfiguration)

Topology updates are event-driven and governed: $T_{t+1} = \begin{cases} \mathcal{R}(T_t, \Delta_T) & \text{if } G(\Delta_T, \text{state}, \text{risk})=\text{ALLOW} \\ T_t & \text{otherwise} \end{cases}$ Tt+1={R(Tt,ΔT)Ttif G(ΔT,state,risk)=ALLOWotherwise

Where:

$\Delta_T$ ΔT is a candidate routing/topology change (swap/rotate/rewire modules)
$\mathcal{R}$ R applies the reconfiguration (Rubik-like combinatorial remap)
$G$ G is the governance gate (constraints + safety + audit)

Critical rule: topology change must be explicit, logged, and reversible (snapshot rollback).

5) Representation of the Hexagon Lattice (HNL)

5.1 Graph Model

Represent HNL as a directed multigraph:

Nodes: $V = \{1,\dots,N\}$ V={1,…,N} (hex tiles)
Edges: $E \subseteq V \times V$ E⊆V×V (interconnect channels)
Each node has compute capacity $c_i$ ci, memory $\mu_i$ μi, failure probability $p_i$ pi

Topology object:

adjacency matrix $A$ A or edge list
routing table $R$ R (optional)
module assignment vector $\sigma$ σ (which functional block runs where)

5.2 Reconfiguration Operators (Rubik-Type)

Define a small set of primitive operators that can compose complex reconfigurations:

RotateCluster(k, dir): rotate routing among k-node cluster
SwapModules(i,j): exchange functional assignments $\sigma_i \leftrightarrow \sigma_j$ σi↔σj
RewireEdge(u,v,u’,v’): replace one channel with another
IsolateNode(i): remove node from routing (fault containment)
RedundancyBoost(region): allocate extra compute to a region under stress

These operators are the “move set” of the lattice.

6) Governance and Safety in Simulation (Non-Optional)

Every step must pass through a control gate:

6.1 Action Gate

Reject/clip actions that violate constraints:

max force/torque, speed
forbidden zones
energy/thermal limits
“do-not-damage” rules

6.2 Reconfiguration Gate

Approve topology changes only if:

within a whitelist of allowed operators
within a reconfiguration budget (rate limit)
risk score below threshold
rollback snapshot created
audit event logged

6.3 Audit Log Schema (Minimum)

For each time step and event:

sensor hash / state summary
chosen action $a_t$ at
gate verdict (ALLOW/DENY) + reason
learning update hash
topology diff (if any): $T_t \rightarrow T_{t+1}$ Tt→Tt+1
anomalies detected

7) Environment Suite (Task Battery)

The simulator must include at least 5 task families to measure generalization:

Locomotion in variable terrain (stairs, rubble, slopes, friction shifts)
Manipulation (pick-place with perturbations; deformable objects optional)
Tool-use (lever, screwdriver-like motion, latch operations)
Navigation under partial observability (occlusions, sensor dropout)
Recovery under damage (actuator weakening, sensor failure, node loss in HNL)

Each task is parameterized so we can generate infinite variations.

8) Experimental Design and Ablations

You must run controlled comparisons:

Baselines

B0: Static DNN policy (no online learning)
B1: Online learning only (update W; fixed T)
B2: Topology adaptation only (update T; frozen W)
B3: Dual plasticity (update W + T)
B4: Dual plasticity + governance (full RC-ADF gates) ← target configuration

Stress Tests

Sensor dropout 10–40%
Actuator degradation 10–30%
HNL node loss 5–20%
Adversarial perturbations to observations

9) Metrics (What “Success” Means)

9.1 Adaptation and Transfer

Learning speed: episodes to reach threshold performance
Transfer efficiency: performance on new task without retrain / with limited steps
Catastrophic forgetting index: retention after learning new tasks

9.2 Robustness

Fault tolerance curve: performance vs % node loss
Graceful degradation score: slope of decline under damage
Recovery time: steps to regain stable behavior after shock

9.3 Efficiency

Energy proxy: compute cost + actuation cost per success
Reconfiguration overhead: number of topology moves + latency proxy

9.4 Safety / Control

Constraint violation rate (target: 0)
Gate intervention rate (how often action is clipped/denied)
Audit completeness (target: 100%)

10) Simulation Loop (Algorithm Blueprint)

Outer loop: curriculum + evaluation

Initialize environment distribution
For each training epoch:
- sample task parameters
- run episodes
- log metrics
Periodic evaluation on held-out tasks
Stress tests and red-team scenarios

Inner loop: per time step

Observe $o_t$ ot
Fuse → features $z_t$ zt, update world model $m_t$ mt
Propose action $\tilde{a}_t$ a~t
Action gate: $a_t = G_a(\tilde{a}_t)$ at=Ga(a~t)
Step physics: $(s_{t+1}, b_{t+1}) = F(\cdot)$ (st+1,bt+1)=F(⋅)
Compute reward $r_t$ rt, constraint penalties, anomaly signals
Update weights $W_{t+1}$ Wt+1 (online learning)
If reconfiguration trigger:
- propose $\Delta_T$ ΔT
- Reconfig gate: apply or reject
Audit log commit