GoferAI - Semantic VLA RAG Layer

Gofer AI is a human-to-robot learning platform that transforms everyday demonstration videos into robot-executable intelligence. Instead of manually programming robotic behavior through conventional reinforcement learning, users can record a task using a phone or GoPro. Our system extracts keyframes using OpenCV, identifies task phases, objects, and human motion patterns using multimodal Gemini models, and converts demonstrations into structured semantic memory through a Video RAG architecture. These demonstrations are embedded, tagged, and stored for retrieval, allowing the system to reason over prior tasks and reuse knowledge. The extracted trajectories are converted into canonical action representations, then replayed and augmented in simulation using Isaac Lab and Real2Render2Real for scalable data generation. This pipeline enables behavior cloning, demo-initialized reinforcement learning, and diffusion-based policy training. The result is a robot-ready policy capable of sim-to-real transfer. Gofer AI bridges the gap between human intent and autonomous execution—turning video into intelligence.