Principal Researcher, AI Transformation @ Rainbow Robotics I lead AX for the RBQ quadruped: turning how we develop, test, and ship robots into workflows where AI agents do the heavy lifting.
- Robot knowledge AI — RAG over code, docs, manuals, and history, used by the whole team
- Agents that touch the robot — MCP servers letting AI drive the robot, simulator, and app
- Language → behavior — LLM skills that turn plain instructions into robot actions
- AI-native pipeline — AI code review and CI/CD on every PR
- Proof by shipping — rebuilt the RBQ controller app with AI agents: one codebase, Android · iOS · Desktop · Steam Deck
Before robots: 8 years in games, XR, and digital twins — 4 games and 3 apps shipped, 12-month projects done in 1–3 months with AI.
| graph-RAG-study | Hybrid RAG + MCP server — 9,556-node knowledge graph, 100% hit rate |
| harness-bench | CLI that scores how AI-native your Claude Code setup is (8 axes) |
| context-forge | Bootstrap for AI-augmented repos — 82 curated sources, auto-synced |
| AI_Language | Compressed agent-to-agent protocol — 40.7% fewer tokens |
| Molten | Terminal wrapper built for AI coding agents (Tauri · Rust) |
| Data2Avatar | CCTV → digital twin safety monitoring, 4 pose models, ≤150 ms sync |
| VTS Visualizer | Industrial 3D digital twin — 12 months → 1 month with AI |
More on the portfolio.
LLM Agents RAG MCP Claude Code · ROS 2 DDS WebRTC C++ · React Native TypeScript Python · GitHub Actions Docker · Unity/C#