Kevin Weekly, Ph.D. · Physical AI
Physical AI — from research to shipped product. Perception, robot autonomy, on-device inference, multi-sensor fusion, and the safety-critical embedded software that lets a learned model actually act in the world.
I build systems where machine learning has to survive contact with the real world — running on the robot, on the wearable, on the drone, under hard real-time and safety constraints. Fifteen years across perception, SLAM, autonomy stacks, edge inference, sensor fusion, and the embedded software that holds it all together. Berkeley EECS Ph.D. in estimation; founder of an R&D firm; 12 patents granted or pending.
Why this matters for a frontier lab
The hard part of physical AI isn't the model — it's everything around it: real-time constraints, messy sensors, hardware budgets, and a high bar for safety. That's the work I do.
Edge AI on NVIDIA Jetson — CNN perception and VLM workloads, multi-sensor fusion, ROS2 autonomy — for an autonomous material-handling vehicle with WebRTC teleoperation. Perception engineering on Embark's autonomous-trucking stack. SLAM and long-horizon localization leadership at OMRON, iRobot, and Volley.
Shipped a GridNet-style audio-enhancement model (STFT → CNN/LSTM → iSTFT) to the Qualcomm Hexagon DSP — hand-optimized with HVX SIMD intrinsics, integrated with the QuRTOS kernel for real-time multi-channel inference. Next-gen heart-rate algorithms productized across Fitbit's wrist wearables.
Confinement-safety systems for a consumer robotic mower. A patent portfolio on safety-critical DSLs with WCET analysis, hybrid safety verification, and AI agents for automated code validation — the questions you have to answer before a learned system is allowed to act in the world.
Founded Think Circuits and grew it to $1.7M revenue in three years across 20+ delivered projects in robotics, autonomy, medical, and defense. XPRIZE Autonomous Wildfire Response qualified competitor — drone multi-camera fire detection + gimbaled suppression, two US patents filed. Has led every team I've been on.
Selected work
Ordered roughly by relevance to embodied / physical AI. Many were delivered under NDA — happy to go deeper in conversation.
Full vehicle autonomy stack: CNN-based perception and VLM workloads running on NVIDIA Jetson, multi-sensor fusion, real-time propulsion and steering control, a custom shield PCB, WebRTC video teleoperation, and a browser-based command-and-monitoring interface.
GridNet-style speech-enhancement network (STFT → CNN/LSTM → iSTFT) deployed to Qualcomm Hexagon, hand-optimized with HVX SIMD intrinsics and wired into the QuRTOS kernel for real-time multi-channel processing. The unglamorous half of ML: making it run within the power, latency, and memory budget.
Drone-mounted multi-camera fire-detection system and a drone-based gimbaled fire-suppression platform — fielded as a qualified competitor in the XPRIZE Autonomous Wildfire Response competition (advanced to the finalist-announcement stage). Two US patent applications filed.
Reporting to the Head of Software: scoped and executed cross-functional initiatives across the AV stack, pairing deep research into individual components with integrated solution design — and embedding into critical-path efforts when the schedule demanded it.
Led a four-engineer team building medium- and long-term mapping and localization technology for industrial autonomous mobile robots; key contributor to AMR-division strategy, requirements, system architecture, and evaluation.
Technical lead of a four-person localization team: multi-sensor SLAM, confinement-safety logic, and algorithm regression testing for the Terra robotic lawnmower. Principal author of the Python analytics framework used for post-mission log analysis and algorithm evaluation.
Built a 1,500-lb AGV that lifts and transports cars: two custom PCBs, STM32 firmware, and ROS-based control software — including techniques to park 7×14-ft steel trays to within inches.
Led a four-person team advancing current and next-generation wrist-wearable heart-rate algorithms; drove product and release strategy, ran longer-horizon exploratory work, and delivered algorithms, circuits, tooling, and IP that other teams productized.
Multi-year engagement: production C++ prediction library, full nRF52 BLE firmware with bootloader/OTA, PDE-based thermal modeling, Kalman instant-read estimation, and manufacturing programming tools. Shipped to consumers.
STM32 firmware, a RAW/Bayer imaging pipeline with color science, and a full AWS IoT backend (device management, OTA, telemetry, factory provisioning) cost-engineered from 500 to 50,000 devices.
Designed the electronics and on-board software (with software-in-the-loop verification) for a fleet of robotic floating sensors studying San-Joaquin delta hydrodynamics; built a robust collision-avoidance and path-selection controller; helped assemble 140 robots and run field studies.
Demos & videos
Walkthroughs and footage of robots, autonomy stacks, and on-device ML in action.
Video demos of autonomy stacks, edge-AI pipelines, and hardware builds are being prepared for this section.
Experience
Education
Awards & honors
What I work with
ROS / ROS2 autonomy stacks · multi-sensor SLAM & localization · CNN perception · VLM workloads · sensor fusion (Kalman / particle filters) · AprilTag visual fiducials · real-time control · teleoperation (WebRTC)
On-device inference · model deployment to DSP / NPU (Qualcomm Hexagon, HVX SIMD) · NVIDIA Jetson · STFT/iSTFT + CNN/LSTM audio models · wearable biosignal algorithms · post-deployment log analytics
Confinement / functional safety · WCET analysis · regression & verification frameworks · safety-critical DSL design · AI-assisted code validation · FDA & BLE-SIG regulatory pathways
C / C++ / Python · Nordic nRF5x · STM32 · ARM Cortex-M · RTOS (QuRTOS) · BLE incl. bootloader/OTA · PCB & sensor integration · AWS IoT at 50k+ devices · PDE thermal modeling · color science / imaging pipelines
Patents & publications
Patents from Think Circuits, Fitbit, and others; peer-reviewed work from a Berkeley Ph.D. in estimation and field robotics.
Plus conference papers (ICRA, CASE, DCoSS, ICNP, UbiComp) and a decade of invited talks. Full list available on request.
Let's talk
Open to conversations with frontier AI labs working on embodied agents, robotics, and edge deployment — full-time or advisory.