Max Robotics — Global Service Robot Directory + US Certification

About

Michele Persiani is a researcher at the intersection of cognitive robotics, human-robot interaction, and artificial intelligence, with a focus on endowing machines with human-like reasoning capabilities. His work centers on integrating cognitive models—such as working memory and Theory of Mind—into robotic systems to enhance their ability to understand and predict human intentions. In his most-cited paper, "A working memory model improves cognitive control in agents and robots" (2018, 16 citations), Persiani demonstrates how incorporating a working memory framework can significantly boost an agent's cognitive control, enabling more adaptive and context-aware behavior. He further advances human-robot collaboration in "Towards We-intentional Human-Robot Interaction using Theory of Mind and Hierarchical Task Network" (2021, 4 citations), exploring how robots can achieve shared goals through mutual understanding. Persiani also tackles semantic generalization in robotics with "Unsupervised Inference of Object Affordance from Text Corpora" (2019, 4 citations), proposing a method to infer object affordances from textual data without manual specification. His research on "Intent Recognition from Speech and Plan Recognition" (2020, 2 citations) bridges natural language processing and plan recognition, paving the way for more intuitive human-robot communication. With a growing citation impact, Persiani’s contributions are shaping the future of socially aware and cognitively plausible robotic systems.