Max Robotics — Global Service Robot Directory + US Certification

About

Takaaki Sugiyama’s research lies at the intersection of human-robot interaction, dialogue systems, and machine learning, with a particular focus on enabling robots to navigate the complexities of multi-party, real-world conversations. His core contribution is developing computational models that allow robots to estimate “response obligation”—the critical ability to determine when a sound or utterance from a user actually requires a reaction. This is especially challenging in noisy public spaces where a robot must filter out background chatter and decide whether to engage with one person among many. Sugiyama’s most cited work (2015, 10 citations) pioneered a machine learning-based method for this task, addressing a fundamental bottleneck in social robotics. He further refined this approach by incorporating user states (2016, 2 citations) and evaluating models that predict when people will speak to a humanoid robot (2016, 2 citations). While his citation counts are modest, his work tackles a deeply practical problem: preventing robots from either ignoring a legitimate request or awkwardly responding to irrelevant noise. By focusing on the subtle cues of human conversational dynamics, Sugiyama’s research provides a crucial stepping stone toward more natural, socially aware robots capable of seamless interaction in crowded environments.