Name: Simbotic AI
Type: 3D Simulation & AI Platform
Tech Stack: Unreal Engine (C++), Python, Docker
Simbotic AI is a next-generation simulation engine built for training AI agents in controlled, photorealistic 3D environments. This platform helps bridge the reality gap by generating high-fidelity synthetic data in scalable, scenario-based environments that simulate real-world complexity.
🔧 Key Features
🧪 Synthetic Data Generation Engine
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Users can create dynamic 3D scenarios where AI agents interact with their environment
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The system generates synthetic training data (images, behavior logs, object interactions)
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Trainers can simulate edge cases and rare situations, enhancing model robustness
🔲 Dockerized Simulation Stack
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Entire simulation engine is containerized using Docker
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Allows reproducible, scalable training environments
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Enables modelers and AI engineers to define scenes and agents via config files or scripts
🚧 Technical Challenges
🏗️ Leveraging Unreal Engine for Procedural Environment Creation
One of the core challenges was making Unreal Engine’s powerful visual features accessible for non-developer users:
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Created a backend toolchain that converts config files (Python or JSON) into dynamic Unreal scenes
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Integrated AI agent pathing, state logic, and behavioral simulation directly into the engine
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Enabled scene composition, lighting setups, object spawning, and scripted interactions — all runtime programmable
📚 What I Learned
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How to build a modular simulation platform that blends game development, AI tooling, and real-time rendering
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Deepened my experience with Unreal Engine C++ APIs and performance tuning for simulation loops
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Learned how to bridge tools used by AI researchers and 3D artists into a common, usable workflow
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Built pipelines for automated data generation and export used for training computer vision models and reinforcement learning agents