Max Robotics — Global Service Robot Directory + US Certification

About

Francesco Puja’s research lies at the intersection of robotics, computer vision, and human-robot interaction, with a focus on enabling robots to perceive, understand, and act in unstructured environments. His major contributions include developing frameworks for visual search and recognition that allow robots to actively locate task-relevant targets during execution and monitoring, as well as advancing obstacle detection through active exploration—overcoming the limitations of fixed LiDAR sensors on mobile humanoid robots. In human motion analysis, Puja introduced a novel method for automatic discovery and recognition of motion primitives from motion capture data, using “motion flux” to identify key skeletal joint movements. His work on transfer and continual supervised learning for robotic grasping demonstrates how grasping features can be reused across tasks, improving adaptability. With over 16 citations across his most-cited papers, Puja’s research is steadily gaining recognition for its practical contributions to autonomous robotics. His achievements include pioneering active exploration techniques that enhance robot safety in cluttered spaces, making his work valuable for students and researchers interested in embodied AI, perception, and learning systems.