Add Agentic AGI framework for general robotics

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*.pyo
+*.pyd
+.Python
+*.egg
+*.egg-info/
+dist/
+build/
+eggs/
+parts/
+var/
+sdist/
+develop-eggs/
+.installed.cfg
+lib/
+lib64/
+
+# Virtual environments
+.env
+.venv
+env/
+venv/
+
+# Testing
+.pytest_cache/
+.coverage
+htmlcov/
+.tox/
+
+# IDEs
+.vscode/
+.idea/
+*.swp
+*.swo
+
+# Models (large binary files)
+models/*.pt
+models/*.pth
+models/*.onnx
+
+# Logs
+*.log
+logs/
+
+# OS
+.DS_Store
+Thumbs.db

Dockerfile

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+# Dockerfile – rag7 AGI Robotics Framework
+# Multi-stage build: install deps then copy source.
+
+# ---------------------------------------------------------------------------
+# Stage 1: Builder – install Python dependencies
+# ---------------------------------------------------------------------------
+FROM python:3.10-slim AS builder
+
+WORKDIR /build
+
+# Install system build tools
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    gcc \
+    g++ \
+    cmake \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install Python dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir --user \
+    torch>=2.0.0 \
+    torchvision>=0.15.0 \
+    numpy>=1.24.0 \
+    scipy>=1.10.0 \
+    opencv-python-headless>=4.7.0 \
+    pyyaml>=6.0 \
+    pytest>=7.0.0 \
+    pytest-cov>=4.0.0
+
+# ---------------------------------------------------------------------------
+# Stage 2: Runtime – lean image with the application
+# ---------------------------------------------------------------------------
+FROM python:3.10-slim AS runtime
+
+WORKDIR /app
+
+# Copy installed Python packages from the builder stage
+COPY --from=builder /root/.local /root/.local
+
+# Install minimal runtime system libraries (OpenCV needs libGL)
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy source code
+COPY . .
+
+# Ensure user-installed packages are on the path
+ENV PATH="/root/.local/bin:$PATH"
+ENV PYTHONPATH="/app"
+
+# Default command: run the test suite
+CMD ["python", "-m", "pytest", "tests/", "-v", "--tb=short"]

README.md

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-# rag7
+# rag7 – Agentic AGI Robotics Framework
+
+![Python 3.10+](https://img.shields.io/badge/python-3.10%2B-blue)
+![License MIT](https://img.shields.io/badge/license-MIT-green)
+![Tests](https://img.shields.io/badge/tests-pytest-orange)
+![ROS2 Optional](https://img.shields.io/badge/ROS2-optional-lightgrey)
+![PyTorch](https://img.shields.io/badge/PyTorch-2.0%2B-red)
+
+A production-quality, multi-agent AGI robotics framework that integrates
+perception, planning, natural language control, reinforcement learning, and
+ROS2 communication into a unified, extensible system.
+
+---
+
+## Features
+
+- **Multi-agent architecture** – PerceptionAgent, PlanningAgent, ControlAgent,
+  CommunicationAgent, CoordinationAgent, all built on a common `BaseAgent`
+  abstract class with perceive → reason → act loops.
+- **Natural language control** – Parse free-text operator commands into
+  structured robot actions via intent classification and LLM integration
+  (LangChain / GPT-4, with a rule-based fallback).
+- **Modular perception** – RGB-D object detection, semantic segmentation,
+  IoU multi-object tracking, occupancy-grid SLAM, and sensor fusion.
+- **Intelligent planning** – A* path planning, joint-space motion planning
+  with IK/FK, LLM-powered task decomposition, and safety-aware decision making.
+- **Reinforcement learning** – DQN, PPO, experience replay, and behavioural
+  cloning trainers built on PyTorch.
+- **ROS2 integration** – Optional rclpy node wrappers for every agent;
+  graceful fallback to mock mode when ROS2 is not installed.
+- **Simulation** – Gazebo-compatible mock environment for policy development.
+
+---
+
+## Quick Start
+
+```python
+from agents.robotics_agi import RoboticsAGI
+
+agi = RoboticsAGI(config_path="config")
+
+# Natural language control
+result = agi.execute_command("navigate to the charging station")
+print(result["response"])  # "Understood. I will navigate to the specified location."
+
+# Task API
+task = agi.create_task("grasp", target_object="red_cube")
+result = agi.execute_task(task)
+print(result)  # {"success": True, "details": "Grasped 'red_cube'"}
+
+# System status
+print(agi.get_status()["system"])  # "online"
+```
+
+---
+
+## Installation
+
+```bash
+git clone https://github.com/your-org/rag7.git
+cd rag7
+pip install -e .                   # core only
+pip install -e ".[llm,dev]"        # + LangChain + testing tools
+```
+
+See [docs/installation.md](docs/installation.md) for full instructions
+including ROS2 setup and Docker.
+
+---
+
+## Project Structure
+
+```
+rag7/
+├── agents/                  # Core agent classes
+│   ├── base_agent.py
+│   ├── perception_agent.py
+│   ├── planning_agent.py
+│   ├── control_agent.py
+│   ├── communication_agent.py
+│   ├── coordination_agent.py
+│   └── robotics_agi.py      # Top-level orchestrator
+├── perception/              # Sensor processing
+│   ├── vision/              # Detection, segmentation, tracking
+│   ├── slam/                # Mapping & localisation
+│   └── sensor_fusion.py
+├── planning/                # Task, path, motion planning
+├── nlp/                     # NLP pipeline
+├── learning/                # RL & imitation learning
+│   ├── rl/                  # DQN, PPO, ReplayBuffer
+│   └── imitation/           # Behavioural cloning
+├── ros2_interface/          # ROS2 node wrappers
+│   ├── ros2_nodes/
+│   └── launch/
+├── simulation/              # Gazebo environment
+├── config/                  # YAML configuration files
+├── examples/                # Runnable example scripts
+├── tests/                   # pytest test suite
+├── docs/                    # Documentation
+├── Dockerfile
+├── requirements.txt
+└── setup.py
+```
+
+---
+
+## Architecture Overview
+
+The system follows a hierarchical multi-agent architecture:
+
+```
+Operator NL input
+       │
+       ▼
+CommunicationAgent ──► parse intent & entities
+       │
+       ▼
+PlanningAgent ──► decompose goal into steps
+       │
+       ▼
+ControlAgent ──► execute steps (navigate / grasp / stop)
+       │
+       ▼
+ROS2Interface (optional) ──► robot hardware
+```
+
+See [docs/architecture.md](docs/architecture.md) for the full diagram.
+
+---
+
+## Usage Examples
+
+### Navigation
+
+```python
+agi = RoboticsAGI(config_path="config")
+agi.execute_command("go to the storage room")
+```
+
+### Object Manipulation
+
+```python
+agi.execute_command("pick up the blue box and place it on shelf B")
+```
+
+### Multi-Robot Coordination
+
+```python
+from agents.control_agent import ControlAgent
+
+robot_a = ControlAgent(config={})
+robot_b = ControlAgent(config={})
+task = agi.create_task("navigate", location={"x": 5.0, "y": 5.0})
+agi.coordinate_robots([robot_a, robot_b], task)
+```
+
+### Reinforcement Learning
+
+```python
+from learning.rl.dqn_agent import DQNAgent
+from learning.rl.replay_buffer import ReplayBuffer
+
+agent = DQNAgent(state_dim=8, action_dim=4)
+buffer = ReplayBuffer(capacity=10000)
+buffer.push(state, action, reward, next_state, done)
+batch = buffer.sample(32)
+loss = agent.update(batch)
+```
+
+---
+
+## Configuration
+
+All runtime parameters live in `config/`:
+
+| File | Description |
+|------|-------------|
+| `robot_config.yaml` | Sensor topics, dimensions, safety thresholds |
+| `agent_config.yaml` | Per-agent update rates, tolerances, memory |
+| `llm_config.yaml` | LLM provider, model, temperature, API key env var |
+
+---
+
+## Running Tests
+
+```bash
+python -m pytest tests/ -v
+python -m pytest tests/ -v --cov=. --cov-report=html
+```
+
+---
+
+## Contributing
+
+1. Fork the repository and create a feature branch.
+2. Follow PEP 8 and add docstrings to all public classes and functions.
+3. Add or update tests for any changed behaviour.
+4. Open a pull request with a clear description.
+
+---
+
+## License
+
+MIT License – see [LICENSE](LICENSE) for details.
+
+© 2024 Stacey Williams

agents/__init__.py

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+"""
+Agents package for the RAG7 AGI Robotics Framework.
+
+This package provides the core agent classes that implement
+perceive-reason-act loops for autonomous robot control.
+"""
+
+from agents.base_agent import BaseAgent, AgentState
+from agents.perception_agent import PerceptionAgent
+from agents.planning_agent import PlanningAgent
+from agents.control_agent import ControlAgent
+from agents.communication_agent import CommunicationAgent
+from agents.coordination_agent import CoordinationAgent
+from agents.robotics_agi import RoboticsAGI
+
+__all__ = [
+    "BaseAgent",
+    "AgentState",
+    "PerceptionAgent",
+    "PlanningAgent",
+    "ControlAgent",
+    "CommunicationAgent",
+    "CoordinationAgent",
+    "RoboticsAGI",
+]