Greg Foderaro
Papers
4
Total Citations
51
H-Index
4
About
Greg Foderaro’s research lies at the intersection of optimal control, robotics, and artificial intelligence, with a focus on solving complex planning and coordination problems in dynamic environments. His most significant contribution is the development of a generalized reduced gradient (GRG) method for distributed optimal control of very-large-scale robotic (VLSR) systems, published in 2017 and cited 27 times. This work provides a scalable, indirect approach to optimal planning for swarms of robots operating in intricate settings, enabling efficient coordination without centralized computation—a breakthrough for applications like search-and-rescue or environmental monitoring. Foderaro also made notable strides in pursuit-evasion path planning, using model-based cell decomposition and approximate λ-policy iteration to optimize online evasive strategies. His work on the video game Ms. Pac-Man, with papers accumulating 16 and 4 citations, serves as a compelling testbed for these algorithms, demonstrating real-time decision-making under adversarial pressure. By bridging theoretical control methods with practical, game-inspired challenges, Foderaro has advanced the field of autonomous navigation, offering tools that are both mathematically rigorous and computationally feasible for large-scale robotic systems.
Research Focus
Key Achievements
Top Papers
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