Kazuki Yane
Papers
2
Total Citations
11
H-Index
2
About
Kazuki Yane is a robotics researcher focused on advancing environmental recognition for robotic systems, particularly through the lens of physical interaction rather than traditional vision-based methods. His work centers on enabling robots to perceive and adapt to the impedance properties—such as stiffness and viscosity—of objects and environments, a critical capability for safe and effective tele-operation and autonomous manipulation. In his highly cited 2022 paper, "Recognition of Environmental Impedance Configuration by Neural Network Using Time-Series Contact State Response," Yane introduced a novel approach that uses neural networks to interpret time-series contact data, allowing robots to infer physical properties that cameras cannot capture. This work, with 9 citations, lays the groundwork for robots to generate context-appropriate motions and assess situational dynamics. His 2024 study, "Probing Signal Injection for Compatible Realization of Operationality and Impedance Identification," further refines this concept by demonstrating how deliberate probing signals can simultaneously maintain operational performance while identifying environmental impedance. Yane’s contributions are pivotal for advancing haptic feedback and stable human-robot interaction, offering a tangible path toward robots that can feel and understand their surroundings as intuitively as humans do.
Research Focus
Key Achievements
Top Papers
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