Max Robotics — Global Service Robot Directory + US Certification

About

Tadashi Nunobiki’s research lies at the intersection of human-robot interaction and socially intelligent robotics, with a focus on designing autonomous systems that can engage people naturally and comfortably in public spaces. His work addresses critical challenges in how robots perceive, predict, and respond to human behavior during real-world encounters. In his most cited paper, “Decision-Making Prediction for Human-Robot Engagement between Pedestrian and Robot Receptionist” (2018, 9 citations), Nunobiki explores how robots can anticipate a pedestrian’s intentions to initiate smoother, more intuitive interactions. He further advances this line of inquiry in “Can User-Centered Reinforcement Learning Allow a Robot to Attract Passersby without Causing Discomfort?” (2019, 3 citations), where he applies reinforcement learning to minimize user discomfort while capturing attention—a key step toward socially acceptable robot behavior. Additionally, his work on “Authoring Robot Presentation for Promoting Reflection on Presentation Scenario” (2019, 5 citations) extends his expertise into non-verbal communication, helping robots and human presenters alike improve their expressive gestures. Though his citation counts are modest, Nunobiki’s contributions are notable for their user-centered, real-world focus, laying groundwork for more empathetic and context-aware social robots.