Chris Paxton
AI Innovation Lead
Pittsburgh, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolChris Paxton is an AI Innovation Lead with 12 years of experience advancing robot motion and task planning by tightly integrating perception, learning, and practical system engineering. He has driven embodied AI research and productization across Meta, NVIDIA, Hello Robot, and now Agility Robotics, blending PhD-level research from Johns Hopkins with hands-on work on real robots like UR5 and KUKA iiwa. Chris contributes to open-source robotics tooling—his work on long-lived hand-eye calibration code and Meta’s home-robot stack shows a practical focus on reproducible calibration, ROS integration, and vision/ML tooling such as CLIP. Colleagues rely on him to turn academic algorithms into reliable robot behaviors and demos that run on physical hardware. Based in Pittsburgh, he’s equally comfortable writing CMake and OpenCV integrations as he is leading cross-functional teams to build robots that assist people in real-world settings.
12 years of coding experience
12 years of employment as a software developer
Johns Hopkins UniversityBachelor of Science (BS) Computer Science, Bachelor of Science (BS) Computer Science at University of Maryland
English
Github Skills (17)
opencv10
robotics10
python10
configuration-management10
cmake10
robot10
eigen10
calibration10
computer-vision10
ros10
operating-system10
translation9
c-language9
rotation9
cprogramming-language9
Programming languages (10)
Github contributions (5)
facebookresearch/home-robot
Nov 2022 - Mar 2023
Mobile manipulation research tools for roboticists
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:1 release, 798 reviews, 143 commits in 4 months
Contributions summary:Chris's contributions primarily revolve around implementing and refining features related to mobile manipulation research tools for roboticists. Their work includes updating configurations, integrating new files, and modifying demo code to work with real robots. They also added dependencies like OpenAI-CLIP and visualization tools, suggesting a focus on computer vision and possibly machine learning integration. Furthermore, the user fixed ROS configuration and setup the visual setup which indicates their involvement in integrating the software with the robotic hardware.
jhu-lcsr/handeye_calib_camodocal
Apr 2016 - Feb 2017
Easy to use and accurate hand eye calibration which has been working reliably for years (2016-present) with kinect, kinectv2, rgbd cameras, optical trackers, and several robots including the ur5 and kuka iiwa.
Role in this project:Back-end Developer
Back-end DeveloperContributions:13 commits, 3 pushes, 1 branch in 10 months
Contributions summary:Chris primarily contributed to the hand-eye calibration project by implementing debugging output and improving the build process. They modified the `CMakeLists.txt` file to ensure correct compilation. Additionally, the user integrated OpenCV libraries for transform file read/write functionality and calibrated transform estimation. Their work included refactoring existing code and adding logging for better transform analysis.
rotationcameratranslationkukarobots
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