ra3-advisor 1.0.0
Reality-Aware Autonomous Advisor: A Neuro-Symbolic AI Framework
Requires Python: >=3.10

# RA³: Reality-Aware Autonomous Advisor

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![Architecture: Neuro-Symbolic](https://img.shields.io/badge/Architecture-Neuro--Symbolic-red.svg)]()

RA³ is a modular framework for autonomous intelligence that bridges the gap between statistical machine learning and symbolic logic. Developed by **Muqarab Nazir**, this framework is designed to sense reality, reason through logic, and act safely in physical environments.

### 📄 Official Docs
- **Research Paper**: [RESEARCH_PAPER.md](./RESEARCH_PAPER.md)
- **LinkedIn Strategy**: [LINKEDIN_STRATEGY.md](./LINKEDIN_STRATEGY.md)

## 🧠 Core Methodology

RA³ operates on a continuous **SENSE → REASON → ACT → LEARN** loop:

1.  **Multimodal Perception**: Real-time integration of visual feeds, vibration sensors, and proximity data.
2.  **Neuro-Symbolic Reasoning**: Logical inference engines that check autonomous decisions against strict safety rules.
3.  **Actionable Decision Layer**: A hybrid system that balances goal optimization with safety-first fallbacks.
4.  **Online Learning**: Continual adaptation to environmental changes using incremental machine learning (River/Avalanche).

## 🚀 Key Features

- **Grounded Vision (Path A)**: Real-time YOLOv8 object detection integrated directly into reasoning.
- **Goal Pursuit Engine (Path B)**: Autonomous navigation with dynamic steering and interactive tactical radar.
- **Safety Interlocks (Path C)**: Automatic pausing, evidence snapshotting, and manual reset protocols.
- **Voice Intelligence (Path D)**: Real-time verbal feedback on system status and reasoning alerts.
- **A* Tactical Pathfinding (Path E)**: Advanced waypoint generation for navigating complex obstacle fields.
- **ROS2 Bridge (Path F)**: Standardized messaging for physical hardware integration.
- **Transparent Autonomy**: Logic logs explain *why* an action was taken, down to the symbolic rule.
- **Mission Control Dashboard**: A high-fidelity, real-time visualization of the AI's "thought process."

## 🛠️ Tech Stack

- **Reasoning**: Neuro-Symbolic Logic, Logic Tensor Networks.
- **Learning**: River (Online Machine Learning), Stable Baselines3 (RL).
- **Backend**: FastAPI, WebSockets, Python.
- **Dashboard**: React 19, Vite, Tailwind CSS, Framer Motion, Recharts.

## 📖 Get Started

### Prerequisites
- Python 3.10+
- Node.js & npm

### Installation & Execution
See the [Walkthrough Guide](file:///C:/Users/SIA/.gemini/antigravity/brain/0bf852da-dc0c-4278-b108-283b49653e77/walkthrough.md) for detailed setup instructions.

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## 🔬 Research & Publication
The theoretical foundations and technical architecture of the RA³ framework are detailed in the [RESEARCH_PAPER.md](./RESEARCH_PAPER.md).

## 🚀 Public Launch & Branding
For a guide on recording a demo and the official LinkedIn launch strategy, see [LINKEDIN_STRATEGY.md](./LINKEDIN_STRATEGY.md).

## 🤝 Contribution
RA³ is an open framework. We welcome researchers and engineers to contribute to the evolution of grounded, logical autonomy.

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**Founder & Architect**: Muqarab Nazir
**Lead Implementation**: Antigravity