Andreas Pas
Senior Robotics Engineer at Luminous
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolAndreas Pas is a Senior Robotics Engineer with 12 years of experience combining academic rigor—a PhD in Computer Science from Northeastern—with hands-on robotics vision and manipulation work. He specializes in computer-vision-driven grasping for unstructured environments, repeatedly taking algorithms from research prototypes to real robotic platforms at startups and labs. His engineering contributions include improving performance and visualization in a 6-DOF grasp detection system on GitHub, reflecting a practical focus on CPU/GPU resource optimization and robust sampling. Andreas has led vision teams and shipped research-driven products at companies like BotBuilt and Jacobi Robotics, and has internships/consulting experience at TRL, JPL, and Toyota Research Institute. Based in Boston, he blends deep perception expertise with an appetite for iterating fast on physical systems to solve messy real-world manipulation problems.
12 years of coding experience
2 years of employment as a software developer
Master of Science - MS, Artificial Intelligence, cum laude, Master of Science - MS, Artificial Intelligence, cum laude at Maastricht UniversityDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Northeastern UniversityUniversity of Electronic Science and Technology of China
German, English, French
Github Skills (6)
computer-vision10
caffe10
robotics10
ros10
machine-learning9
python8
Programming languages (4)
Github contributions (5)
atenpas/gpd
May 2017 - Jul 2021
Detect 6-DOF grasp poses in point clouds
Role in this project:
ML Engineer
ML EngineerContributions:3 releases, 70 commits, 14 PRs in 4 years 2 months
Contributions summary:Andreas primarily contributed to the development of the grasp detection system, focusing on integrating a Caffe-based classifier for grasp evaluation. They modified the code to incorporate parameters for CPU/GPU usage, suggesting an emphasis on performance and resource optimization. Additional changes involved fixing parameter names and adjusting sampling parameters, improving the system's functionality and potentially its grasp detection accuracy. Furthermore, the user updated the visualization component to correctly visualize the grasp results.
dofgrasproboticspoint-cloudspoint
atenpas/agile_grasp-release
Apr 2015 - May 2015
Contributions:125 pushes, 30 branches in 1 month
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