Max Robotics — Global Service Robot Directory + US Certification

About

Michael Greenspan is a roboticist whose career has been defined by a fascinating blend of fundamental safety research and playful, high-profile engineering challenges. His foundational work in motion planning produced the "MPK: An open extensible motion planning kernel," a software system that has served as a critical tool for developing and benchmarking geometric reasoning algorithms. He made a significant contribution to teleoperation safety with his work on "Obstacle count independent real-time collision avoidance," a method for ensuring safe, real-time robotic manipulation in cluttered environments. However, Greenspan is perhaps best known for his "Deep Green" project, a vision-based robotic system designed to play pool at a championship level. This ambitious endeavor, detailed in papers like "Robotic pool: an experiment in automatic potting" (23 citations) and "Toward a Competitive Pool-Playing Robot" (17 citations), required solving complex problems in computer vision, physics simulation, and precision control. His "Plan-N-Scan" system (33 citations) for autonomous workspace mapping further demonstrates his versatility. With over 180 citations across his most-cited works, Greenspan’s legacy is one of a researcher who could tackle both the rigorous demands of safety-critical systems and the captivating challenge of building a world-class robotic billiards player.