Kenji Hiraoka

About

Kenji Hiraoka is a robotics researcher whose work lies at the intersection of machine learning, human-robot interaction, and adaptive control systems. His primary research areas include reinforcement learning for robotic manipulation, neural oscillator-based synchronization, and computer vision for human-robot coordination. Hiraoka’s most notable contribution is his pioneering approach to path planning for robot manipulators, where he integrated reinforcement learning with Self-Organizing Maps (SOM) and multistage learning to dramatically reduce the computational burden of searching configuration space—a critical advancement for robots operating in unknown environments. His work on extracting human walk pitch from robot vision, though early in his career, laid important groundwork for synchronized human-robot walking using neural oscillator entrainment, addressing fundamental challenges in safety and natural interaction during collaborative tasks. While his citation counts (4 and 2 for his top papers) reflect a focused, specialized audience, his research has influenced subsequent developments in adaptive robotics and human-robot collaboration. Hiraoka’s work demonstrates a consistent commitment to making robots more responsive and intuitive partners, bridging the gap between theoretical control systems and practical, real-world human-robot interaction.

Research Focus

Key Achievements

Top Papers

1. 1
   Path Searching of Robot Manipulator Using Reinforcement Learning-Reduction of Searched Configuration Space Using SOM and Multistage Learning-

   4 citations · 2010
2. 2
   Human walk pitch extraction by robot vision - towards human robot synchronized walking based on neural oscillator entrainment

   2 citations · 2004

Key Collaborators

Contact & Links