Papers
40
Total Citations
635
H-Index
15
About
Masashi Hamaya is a versatile robotics researcher whose work spans wearable robotics, robotic manipulation, and AI-driven task planning. His early career focused on exoskeleton systems, where he made significant contributions to balance control and human-robot interaction. His 2015 paper on variable ankle stiffness for bipedal exoskeletons (86 citations) introduced innovative real-time balance techniques, while his 2017 work on learning assistive strategies from physical interaction (79 citations) addressed the growing societal need for intelligent wearable robots in aging populations. He further advanced the field through brain-machine interfaces for exoskeleton control and soft actuator identification methods. Hamaya subsequently pivoted toward industrial robotic manipulation, developing techniques for in-hand pose estimation, soft robotic wrists, and assembly task learning that leverage environmental constraints and physical compliance to reduce engineering complexity. His work on sample-efficient deformable object manipulation reflects a commitment to practical, real-world applicability. More recently, he has embraced large language models for robot task planning, with his 2024 Vision-Language Interpreter paper (35 citations) bridging symbolic planning and multimodal AI. Across more than a decade of research, Hamaya's contributions demonstrate a rare breadth, consistently pushing robotics toward greater adaptability, intelligence, and human compatibility.
Research Focus
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Top Papers
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- 3Vision-Language Interpreter for Robot Task Planning35 citations · 2024
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