{"repo":"AdnanSattar/Spatial-RAG-Worldmodel","free":true,"listed":false,"github":"https://github.com/AdnanSattar/Spatial-RAG-Worldmodel","clone":"git clone https://github.com/AdnanSattar/Spatial-RAG-Worldmodel.git","description":"A Spatial Retrieval-Augmented Generation system for latent world models, designed for embodied spatial intelligence in robotics, autonomous navigation, and embodied AI. Features ROS2 integration, real-time inference @ 25Hz, and complete robot build guide.","language":"Python","stars":14,"topics":["autonomous-robots","embodied-ai","latent-space","ros2-humble","spatial-computing","world-models","computer-vision","docker","fastapi","nextjs"],"license":"MIT","category":"machine-learning","readme_excerpt":"Spatial-RAG World Model A Spatial Retrieval-Augmented Generation system for latent world models, designed for embodied spatial intelligence in robotics, autonomous navigation, and embodied AI. 🎯 What is Spatial-RAG? This project implements a memory-augmented latent world model that: - Encodes observations (RGB, depth, proprioception) into compact latent representations - Stores latent states with spatial metadata in a vector database - Retrieves relevant past experiences using hybrid spatial + latent similarity search - Predicts future states by conditioning on retrieved memory context Result: Improved prediction accuracy (15-30%) and sample efficiency for embodied agents. 📖 New to Spatial-RAG? See the Practical Usage Guide for real-world applications and examples. 🏗️ Architecture Key Components: 🚀 Quick Start 1. Installation 2. Docker Setup (Recommended) Access: - 🌐 API : http://localhost:8080 - 📚 API Docs : http://localhost:8080/docs - 🖥️ UI Dashboard : http://localhost:3000 - 🔍 Qdrant : http://localhost:6333 3. Generate Data & Train 4. Test in UI 1. Open http://localhost:3000 2. Click \"Generate Random Latent\" 3. Click \"Start Rollout\" 4. Watch predicted frames stream in real-time! 🤖 Real-World Applications Application Use Case ------------- ---------- 🚁 Autonomous Drones Navigate cities using past flight memories 🚗 Self-Driving Cars Predict pedestrian behavior at intersections 📦 Warehouse Robots Remember item locations for faster picking 🏠 Home Assistants Learn","default_branch":null,"files":null,"tree":[],"storefront":"/r/AdnanSattar","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AdnanSattar/Spatial-RAG-Worldmodel/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}