Closing the gap between
autonomous navigation research and deployed hardware.
Two years shipping ROS 2 mobile robots into production — hospitals, restaurants, factory floors. Currently Source Engineer at VinRobotics and Lab Leader at HalBotLab, researching Embodied AI and Nav2 systems. Published in MethodsX (Q2) on distributional RL for path planning under sensor noise.
Most robotics demos work in ideal conditions. My job is to make them work when they don't — when the LiDAR scan is partial, the floor is reflective, the network drops packets, and the operator expects the robot to keep moving. I tune DDS QoS profiles and ROS 2 executor strategies for real-time performance, configure Cartographer SLAM for constrained hardware, and debug Nav2 failure modes that never appear in simulation.
At ByteHome Robotics, I led the full cycle from prototype to commercialized product — hardware integration, software stack, and the rarely-mentioned last mile: writing operator runbooks, training 30+ non-technical B2B clients to maintain and operate ROS 2 robots without engineering support on-site.
At HalBotLab I'm focused on the harder problem — moving beyond scripted navigation toward robots that understand unstructured 3D environments. We work with depth images, point cloud pipelines, and Micro-ROS on ESP32 running RTOS for low-latency sensor communication.
Looking for roles where the robots actually ship — not just simulate. Particularly interested in teams working on autonomous mobile robots, Embodied AI, or edge-compute robotics stacks where the distance between a ROS 2 node and a physical actuator is short.
Research collaborations welcome: sim-to-real transfer, distributional RL, or perception for navigation. If you're training operators on ROS 2 and need someone who's done it before — that too.
Based in Hanoi, Vietnam.
Open to international roles — remote-first or relocation.