Max Robotics — Global Service Robot Directory + US Certification

About

Joseph Lam is a pioneering roboticist whose work centers on the intersection of computer vision, intelligent systems, and precision manipulation, with a particular focus on developing autonomous agents for complex, real-world games. His most significant contribution is the creation of **Deep Green**, a vision-based robotic pool-playing system designed to challenge proficient human opponents. Lam’s research tackles the full pipeline of robotic gameplay, from high-level strategy to low-level control. His landmark 2008 paper, "Toward a Competitive Pool-Playing Robot," which has garnered **17 citations**, demonstrates a system that already plays at a better-than-amateur level. To achieve the pinpoint accuracy required for championship play, Lam developed an innovative **eye-in-hand visual servoing algorithm** (2008, 3 citations) that corrects absolute positioning errors using a wrist-mounted camera. His work systematically addresses the core question of whether computational intelligence is necessary for robotic pool (2007, 4 citations), and he established rigorous methods for quantifying system performance through shot accuracy analysis (2006, 2 citations). By advancing the state of the art in visual servoing and autonomous decision-making, Lam has created a benchmark platform for studying embodied intelligence in a dynamic, adversarial environment.