Robotics systems
ROS 2 integration, navigation, manipulation, control scaffolds, and sim-first validation.
I’m Obinna Edeh, an AI systems engineer building dependable robotics, edge inference, digital-twin, and operational intelligence systems.
I build operational AI systems for physical and edge environments under real infrastructure constraints.
My background in telecom and wireless operations gives me a systems-level view of AI deployment—especially where telemetry, infrastructure reliability, edge inference, and real-time operational constraints matter.
My current work spans Physical AI, edge inference, computer vision, runtime observability, operational safety, and AI-RAN workflows. Across my flagship projects, I work with Jetson-based inference, ROS 2 robotics, urban vision analytics, network telemetry, and time-series forecasting.
The common thread is simple: AI systems should not just run once in a demo. They should be measurable, observable, reliable, and useful under real constraints. My passion is bridging the sim-to-real gap.
Each project is built around reproducible artifacts, operational evidence, and a clear path to deployment.
A reproducible engineering path from robot description and digital twin to edge inference, telemetry, and safety-aware operations.
Agentic diagnostics over logs, documentation, and robot state—designed to keep recommendations observable and human-bounded.
A 6DOF arm workflow spanning URDF, MoveIt 2, OpenUSD assets, policy-training scaffolds, and evidence-led validation.
ROS 2 integration, navigation, manipulation, control scaffolds, and sim-first validation.
Jetson inference and AI-RAN workflows grounded in latency, telemetry, reliability, power, and thermal evidence.
Isaac Sim and OpenUSD workcells that connect robot assets to repeatable validation.
Human-bounded copilots that reason over telemetry, logs, manuals, and runtime state.