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About

Juhi Gurnani is a robotics researcher focused on advancing the precision and adaptability of industrial robotic systems. Her work centers on three key areas: kinematic calibration, intrinsic dynamics identification, and learning from demonstration for compliant manipulation. Gurnani’s major contributions include developing a fast kinematic re-calibration method for industrial robot arms, addressing the critical gap between ideal manufacturer models and real-world manufacturing errors—a paper that has garnered 29 citations for its practical impact on safe and reliable robotic operations. She has also pioneered techniques to identify intrinsic friction and torque ripple in robotic joints with integrated torque sensors, enabling more accurate force measurement for delicate tasks like wheel-bearing characterization. Additionally, her work on learning compliant box-in-box insertion through haptic-based teleoperation (5 citations) tackles the challenging problem of automating deformable object manipulation in logistics. Gurnani’s research bridges theoretical modeling and real-world application, enhancing robot performance in both contact and non-contact tasks. Her achievements demonstrate a commitment to making industrial robots more robust, intuitive, and capable of handling complex, real-world assembly and packaging challenges.