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About

Misaki Hanafusa is a robotics researcher specializing in human-robot cooperation and physical human-robot interaction, with a focus on making collaborative robots safe, stable, and intuitive to use. Her core research areas include mechanical impedance control, human-adaptive control systems, and the application of recurrent neural networks (RNNs) for external force estimation and human state prediction. Hanafusa’s major contributions lie in developing compliant motion control strategies that allow robots to absorb collision forces during object manipulation and to dynamically adjust their impedance parameters based on estimated human arm stiffness, thereby improving both safety and operability. Her most cited work, "Mechanical Impedance Control of Cooperative Robot During Object Manipulation Based on External Force Estimation Using Recurrent Neural Network" (2020, 11 citations), introduces a novel external force estimator that distinguishes net external forces to enable stable human-robot co-manipulation. She has also advanced the field through human-adaptive impedance control methods that enhance contact stability and nimbleness, as demonstrated in her 2022 study on operability improvement. With a growing body of work that bridges neural network estimation and real-time robot control, Hanafusa is establishing herself as a key contributor to the next generation of safe, responsive collaborative robots.