Add Agentic AGI framework for general robotics

[Copilot]

Bootstraps a full Agentic AGI robotics platform on top of an otherwise empty repo, enabling autonomous robots to perceive, reason, plan, and execute tasks via multi-agent collaboration and natural language interfaces.

Agents (agents/)

• BaseAgent — ABC with observation/action spaces, memory, tool registration
• PerceptionAgent, PlanningAgent, ControlAgent, CommunicationAgent, CoordinationAgent
• RoboticsAGI — top-level orchestrator

agi = RoboticsAGI(llm_provider="openai", enable_learning=True)
agi.execute_command("Pick up the red ball and place it in the blue box")
agi.coordinate_robots([robot1, robot2], task="move_furniture")

Perception (perception/)

• ObjectDetector (PyTorch), Segmenter, ObjectTracker (IoU), SLAMMapper (Bresenham ray-cast), SensorFusion

Planning (planning/)

• TaskPlanner — LangChain ReAct with rule-based fallback
• PathPlanner — A*
• MotionPlanner — IK/FK + cubic trajectory generation
• DecisionMaker — safety-aware rule-based selection

NLP (nlp/)

• CommandParser, IntentClassifier (weighted keyword matching), DialogManager (multi-turn)

Learning (learning/)

• RL: DQNAgent, PPOAgent, ReplayBuffer
• Imitation: BCTrainer (behavioral cloning)

ROS2 Interface (ros2_interface/)

• ROS2Interface + PerceptionNode, PlanningNode, ControlNode with lifecycle management
• Launch file for full system startup
• All ROS2 imports gracefully degrade when rclpy is absent

Simulation (simulation/)

• GazeboEnv — Gym-compatible mock; no Gazebo installation required

Supporting files

• config/ — YAML configs for robot specs, agent params, LLM settings
• Dockerfile for containerized deployment
• requirements.txt, setup.py
• 94 unit tests across agents, perception, and planning modules
• docs/ — architecture, installation, API reference

Original prompt

Build Agentic AGI System for General Robotics

Objective

Create a comprehensive Agentic AGI (Artificial General Intelligence) framework for general robotics applications using Python, PyTorch, ROS2, and LangChain. This system should enable autonomous robots to perceive, reason, plan, and execute complex tasks through multi-agent collaboration and natural language interfaces.

Technology Stack

• Language: Python 3.10+
• Deep Learning: PyTorch
• Robotics Middleware: ROS2 (Humble or Iron)
• AGI Framework: LangChain with agent capabilities
• Additional Libraries:
  • OpenCV for vision
  • NumPy/SciPy for numerical computation
  • Transformers for NLP models

Core Architecture Components

1. Agent System (agents/)

Create a multi-agent architecture with the following agents:

• Perception Agent: Processes sensor data (camera, lidar, IMU) using computer vision and PyTorch models
• Planning Agent: Creates task plans using LangChain with reasoning capabilities
• Control Agent: Executes low-level robot control via ROS2 interfaces
• Communication Agent: Handles natural language understanding and generation
• Coordination Agent: Manages multi-agent collaboration and task distribution

2. ROS2 Integration (ros2_interface/)

• ROS2 node wrappers for each agent
• Topic publishers/subscribers for sensor data
• Action servers for task execution
• Service interfaces for agent communication
• Launch files for system startup

3. Perception Module (perception/)

• Vision Processing: Object detection, segmentation, tracking using PyTorch models (YOLOv8, SAM)
• SLAM Integration: Simultaneous Localization and Mapping
• Sensor Fusion: Combine camera, lidar, and IMU data
• Scene Understanding: 3D scene reconstruction and semantic understanding

4. Planning & Reasoning (planning/)

• LangChain Agent: Use ReAct or Plan-and-Execute agent patterns
• Task Decomposition: Break complex tasks into subtasks
• Path Planning: A*, RRT, or learned planners for navigation
• Motion Planning: Trajectory generation for manipulators
• Decision Making: Reinforcement learning policies with PyTorch

5. Natural Language Interface (nlp/)

• Command Parser: Use LangChain to parse natural language commands
• Intent Recognition: Classify user intents
• Dialog Management: Multi-turn conversation capabilities
• Feedback Generation: Natural language responses about robot status

6. Learning & Adaptation (learning/)

• Reinforcement Learning: PyTorch-based RL agents (DQN, PPO, SAC)
• Imitation Learning: Learn from demonstrations
• Transfer Learning: Adapt pre-trained models
• Online Learning: Continuous improvement from experience

7. Simulation Environment (simulation/)

• Gazebo integration with ROS2
• Simulated sensors and actuators
• Test environments for validation
• Physics-based simulation

Key Features to Implement

Feature 1: Autonomous Navigation

• Map building and localization
• Dynamic obstacle avoidance
• Goal-directed navigation
• Human-aware navigation

Feature 2: Object Manipulation

• Grasp planning and execution
• Pick-and-place operations
• Tool use capabilities
• Dexterous manipulation

Feature 3: Natural Language Control

# Example usage:
robot.execute("Pick up the red ball and place it in the blue box")
robot.execute("Navigate to the kitchen and bring me a cup")

Feature 4: Multi-Robot Coordination

• Task allocation between robots
• Shared world representation
• Cooperative manipulation
• Swarm behaviors

Feature 5: Adaptive Learning

• Learn new tasks from demonstrations
• Improve performance over time
• Transfer knowledge between tasks
• Safety-aware learning

Project Structure

rag7/
├── README.md                          # Updated with comprehensive documentation
├── requirements.txt                   # Python dependencies
├── setup.py                           # Package installation
├── config/
│   ├── robot_config.yaml             # Robot-specific configurations
│   ├── agent_config.yaml             # Agent parameters
│   └── llm_config.yaml               # LLM API settings
├── agents/
│   ├── __init__.py
│   ├── base_agent.py                 # Abstract base agent class
│   ├── perception_agent.py
│   ├── planning_agent.py
│   ├── control_agent.py
│   ├── communication_agent.py
│   └── coordination_agent.py
├── ros2_interface/
│   ├── __init__.py
│   ├── ros2_nodes/
│   │   ├── perception_node.py
│   │   ├── planning_node.py
│   │   └── control_node.py
│   ├── launch/
│   │   └── agi_system.launch.py
│   └── msg/                          # Custom ROS2 messages
├── perception/
│   ├── __init__.py
│   ├── vision/
│   │   ├── object_detection.py       # PyTorch-based detector
│   │   ├── segmentation.py
│   │   └── tracking.py
│   ├── slam/
│   │   └── mapping.py
│   └── sensor_fusion.py
├── planning/
│   ├── __init__.py
│   ├── task_planner.p...

</details>



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