I close the gap between a robot that works in the lab and a fleet that works in the field.
I help teams take hardware and AI from prototype to reliable operation at scale. I have done that across deployment and integration programs at AT&T, Google, Amdocs and T-Mobile, leading engineering teams, field crews and vendors through thousands of field sites. One lesson held on every program: a spec sheet describes ideal conditions; the site shows what the hardware actually does.
Physical AI has the same gap. At EmbodiedEdge, my Physical AI lab, I test robots and edge AI on my own hardware and measure what survives the move from benchmark to field.
Open to senior engineering leadership roles in Physical AI, robotics and edge AI.
- Validate against the site, not the spec sheet.
- Set the budget before the test.
- No claim without its evidence.
- Corrections stay in the record.
- Humans stay in the loop.
Each project links its proof: the measured result, the hardware and the date.
| System | What the evidence shows | Status |
|---|---|---|
| Bench2Field Repository Proof: Case study · Report |
The benchmark said GO. The robot would drop frames. On the rover's Jetson Orin NX the detector alone meets its 33.3 ms deadline (26.9 ms); the full frame takes 47.8 ms. | v1.0.1 released. v2 in progress. |
| Physical AI on Jetson Repository Proof: Case study |
A learned policy running on the robot's own compute. GR00T through TensorRT at 9.8 Hz on Jetson AGX Thor, with a failed evaluation corrected and kept on the record. In simulation, demonstrations that keep mistakes and their corrections took cup placement from 0 to 16 of 50. | Active. |
| Physical AI Safety Observability Repository Proof: Case study · Showcase |
Priced the safety loop before anyone depends on it. A vision-language model on the device costs 4.35 s per frame at 66.6 W. | Functional. Live-camera validation in progress. |
| Urban Edge Vision Analytics Repository Proof: Showcase |
One edge box watching several street cameras. Runs end to end on live cameras; measurement is next. | Functional. |
Simulation, synthetic data and mock adapters are labelled wherever they appear and never presented as deployment proof.
| System | What the evidence shows | Status |
|---|---|---|
| Jetson Edge AI Security Repository Proof: Case study |
Found a power problem in a runtime setting, not the model. Cut a 54.1 W draw on Jetson AGX Thor to idle, 24.3 W, with zero missed deadlines. | Measured. |
| Field Commissioning Copilot Repository available on request Proof: Evidence page |
Built so a technician never acts on an unsourced number. Answers commissioning questions from manufacturer documentation and says when no source covers the question. | Measured locally. |
| Wireless signal AI Four studies |
Learned models measured against classical baselines, limits included: ▸ Neural receiver: wins at moderate SNR, falls behind above 15 dB ▸ KPI forecasting: beats a naive baseline, except over holidays ▸ Link intelligence: neural channel estimation wins at low SNR ▸ Edge telemetry: 100 AGVs fit a 20 ms control budget; 120 don't |
Simulation and public data. |
- Physical AI and robotics: ROS 2, MoveIt 2, Isaac Sim, Isaac Lab, OpenUSD, GR00T, LeRobot, SLAM
- Edge inference: NVIDIA Jetson AGX Thor and Orin NX, TensorRT, ONNX Runtime, CUDA, vLLM
- ML and operational AI: Python, PyTorch, vision-language models, retrieval-grounded copilots, evaluation harnesses
- Infrastructure: Docker, Kubernetes, CI/CD, AWS, Azure, GCP
Newsletter: At the Edge with Obyno · obiedeh@gmail.com · LinkedIn · embodiededge.ai · obiedeh.github.io