git clone https://github.com/YOUR_USERNAME/sod-self-learning-ai.git
cd sod-self-learning-ai
---

## ⚙️ `config/config.yaml`

```yaml
device_name: SmartObserverDevice
hardware:
  platform: NVIDIA Jetson Xavier NX
  gpu_enabled: true
  sensors:
    lidar: true
    gpr: true
    ir: true
    camera: true
    photogrammetry: true

ai_settings:
  inference_engine: TensorRT
  model_format: onnx
  feedback_loop_enabled: true
  continual_learning: true
  reinforcement_learning: true
  self_supervised_learning: true

paths:
  data_dir: "./data/"
  model_dir: "./models/"
  logs: "./logs/"
from sensors import lidar, gpr, ir_camera, camera
from fusion.sensor_fusion import collect_all
from models.continual_learner import ContinualLearner
from models.rl_agent import RLAgent
from runtime.inference_engine import infer
from runtime.feedback_loop import evaluate
from utils.logger import log

# Load your AI model
learner = ContinualLearner.load_model()
agent = RLAgent()
# models/self_learning_loop.py

import torch
import torch.nn as nn
import torch.optim as optim

from fusion.sensor_fusion import collect_all
from models.feature_extractor import FeatureExtractor   # your CNN/Transformer
from models.rl_agent import RLAgent                      # Q-learning / policy net
from models.self_supervised import SSLModule             # contrastive / autoencoder
from models.continual_learner import ContinualLearner
from runtime.feedback_loop import get_reward_signal
from runtime.inference_engine import infer

class SelfLearningEngine:
    def __init__(self, device='cuda'):
        # 1. Core models
        self.fe_extractor = FeatureExtractor().to(device)
        self.rl_agent    = RLAgent().to(device)
        self.ssl_module  = SSLModule().to(device)
        self.cont_learner= ContinualLearner(self.fe_extractor, device=device)

        # 2. Optimizers
        self.opt_ssl = optim.Adam(self.ssl_module.parameters(), lr=1e-4)
        self.opt_cont = optim.Adam(self.fe_extractor.parameters(), lr=1e-5)
        self.device = device

    def loop(self):
        # Sensor fusion + preprocess
        raw_data = collect_all()
        fused, preprocess_meta = self.cont_learner.preprocess(raw_data)

        # Feature extraction
        features = self.fe_extractor(fused)

        # Inference + action
        action = infer(features)
        # (execute_action lives in runtime/controller)
        # from runtime.controller import execute_action
        # result = execute_action(action)

        # Feedback (reward or correction)
        reward, next_state = get_reward_signal(action)

        # 1) Self-Supervised Update
        ssl_loss = self.ssl_module.compute_loss(fused)
        self.opt_ssl.zero_grad()
        ssl_loss.backward()
        self.opt_ssl.step()

        # 2) Reinforcement Update
        rl_loss = self.rl_agent.update(features, reward, next_state)
        # assume update() backpropagates internally

        # 3) Continual Online Update
        cont_loss = self.cont_learner.update_model(fused, labels=None)
        self.opt_cont.zero_grad()
        cont_loss.backward()
        self.opt_cont.step()

        # Logging
        return {
            'ssl_loss': ssl_loss.item(),
            'rl_loss': rl_loss.item(),
            'cont_loss': cont_loss.item(),
            'reward': reward
        }

if __name__ == '__main__':
    engine = SelfLearningEngine()
    while True:
        stats = engine.loop()
        print(f"[Loop] r={stats['reward']:.2f}, ssl={stats['ssl_loss']:.3f}, rl={stats['rl_loss']:.3f}, cont={stats['cont_loss']:.3f}")
# simple CNN stub
import torch.nn as nn

class FeatureExtractor(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(6, 32, 3, padding=1),   # e.g. fused 6-channel input
            nn.ReLU(),
            nn.Conv2d(32, 64, 3, padding=1),
            nn.ReLU()
        )
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.fc   = nn.Linear(64, 128)

    def forward(self, x):
        h = self.conv(x)
        h = self.pool(h).view(x.size(0), -1)
        return self.fc(h)
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

class RLAgent(nn.Module):
    def __init__(self, feature_dim=128, action_dim=10):
        super().__init__()
        self.policy = nn.Sequential(
            nn.Linear(feature_dim, 64),
            nn.ReLU(),
            nn.Linear(64, action_dim)
        )
        self.optimizer = optim.Adam(self.policy.parameters(), lr=1e-4)
        self.gamma = 0.99

    def forward(self, features):
        return F.softmax(self.policy(features), dim=-1)

    def update(self, features, reward, next_state):
        # Simplified policy-gradient step
        probs = self(features)
        logp = torch.log(probs + 1e-8).mean()
        loss = -logp * reward
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        return loss
import torch.nn as nn

class SSLModule(nn.Module):
    def __init__(self):
        super().__init__()
        # e.g. simple autoencoder
        self.encoder = nn.Sequential(
            nn.Conv2d(6, 32, 3, padding=1),
            nn.ReLU(),
            nn.AdaptiveAvgPool2d(8)
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(32, 6, 8, stride=8),
            nn.Sigmoid()
        )

    def forward(self, x):
        z = self.encoder(x)
        return self.decoder(z)

    def compute_loss(self, x):
        recon = self(x)
        return nn.functional.mse_loss(recon, x)
import torch.nn as nn

class ContinualLearner:
    def __init__(self, feature_extractor, device='cuda'):
        self.model = feature_extractor
        self.device = device

    def preprocess(self, raw):
        # normalize, stack sensor channels, etc.
        fused = raw['lidar'] + raw['ir']  # example fusion
        return fused.to(self.device), {}

    def update_model(self, fused, labels=None):
        # simple self-training via pseudo-labels or online loss
        features = self.model(fused)
        # dummy loss: encourage stable features
        loss = (features ** 2).mean()
        return loss


def main_loop():
    while True:
        sensor_data = collect_all()
        features = learner.extract_features(sensor_data)
        decision = infer(features)
        result = execute_action(decision)
        reward = evaluate(result)
        agent.learn_from(reward, sensor_data)
        learner.update_model(sensor_data)

if __name__ == "__main__":
    log("Starting SOD Self-Learning System...")
    main_loop()
Sensor Fusion Visualization Architecture for SOD (Smart Observer Device) powered by Aptara AI

1. Sensor Inputs

Assume real-time input streams from multiple sensors

IR_data = get_infrared_stream()       # Thermal sensor (e.g., FLIR) RGB_data = get_rgb_camera_stream()    # Visible light camera LiDAR_data = get_lidar_pointcloud()   # 3D spatial mapping GPR_data = get_gpr_signal_map()       # Subsurface mapping

2. Calibration (Pseudo)

Align all sensor data to the same spatial frame

IR_aligned = align_to_world(IR_data) RGB_aligned = align_to_world(RGB_data) LiDAR_aligned = align_to_world(LiDAR_data) GPR_aligned = align_to_world(GPR_data)

3. Feature Extraction

features_IR = extract_temperature_features(IR_aligned) features_RGB = extract_visual_features(RGB_aligned) features_LiDAR = extract_geometry(LiDAR_aligned) features_GPR = extract