Max Robotics — Global Service Robot Directory + US Certification

About

Marco Caccamo is a leading researcher in cyber-physical systems, real-time computing, and autonomous robotics. His foundational work on elastic feedback control and handling execution overruns in hard real-time control systems (2002, 70 and 51 citations) introduced groundbreaking methods for adapting task periods dynamically, enabling more flexible and robust scheduling in robotics and visual sensing applications. He further advanced online control optimization through load-driven scheduling (2002, 38 citations), cementing his influence in real-time systems. More recently, Caccamo has made significant contributions to robotic manipulation and autonomous navigation, as demonstrated by his work on 6IMPOSE (2023, 18 citations), which bridges the reality gap in 6D pose estimation for robotic grasping, and flexible gear assembly using visual servoing and force feedback (2023, 8 citations). He has also pioneered deep reinforcement learning for UAV coverage path planning (2023, 6 citations) and multi-agent autonomous racing with RaceMOP (2024, 5 citations). His research on safe AI, including sandboxing DNN-based controllers in stochastic games (2023, 4 citations), addresses critical challenges in deploying trustworthy autonomous systems. With over 200 citations across his most influential works, Caccamo’s career reflects a sustained impact on both theoretical foundations and practical applications in robotics and real-time systems.