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About

Yuya Hakamata is a robotics researcher specializing in humanoid robot motion generation, with a particular focus on whole-body control and inverse kinematics. His work addresses one of the central challenges in humanoid robotics: enabling robots to perform physically demanding, human-like tasks in dynamic, real-world environments such as kitchens and households. Hakamata's most significant contributions center on developing efficient methods for generating stable whole-body motion in humanoid robots. His 2020 paper on pushing and pulling motion generation — his most cited work with 5 citations — introduced a momentum control framework based on analytical inverse kinematics, allowing robots to perform common manipulation tasks like opening doors or moving objects. Complementing this, his earlier work on torso posture regression, published in 2016 and expanded in 2018, tackled the computational challenge of solving whole-body inverse kinematics in real time, enabling robots to adapt fluidly to changing environments across a wide workspace range. While still an emerging body of work with citations in the single digits, Hakamata's research lays important groundwork for practical humanoid robot deployment in everyday settings — a critical frontier as robotics technology moves closer to real-world domestic and industrial applications.