Karanja Kabini
Papers
1
Total Citations
5
H-Index
1
About
Karanja Kabini is a robotics researcher whose work focuses on the intersection of control theory, machine learning, and mechatronic systems. His primary research areas include force-impedance control, model predictive control (MPC), and neural network-based modeling for robotic manipulators. Kabini’s most notable contribution is his 2024 paper on force-impedance control of a 2-DOF planar robot, where he introduced a novel approach that integrates MPC with successive linearization of a neural network model—specifically, a multilayer perceptron (MLP) based on a nonlinear autoregressive model with exogenous input (NARX). This work enables precise, adaptive control of robot dynamics, allowing for safe and compliant interactions with uncertain environments. While still early in his career, his research has already garnered attention, with his top-cited paper accumulating 5 citations. Kabini’s work is particularly significant for advancing the field of human-robot collaboration and industrial automation, where accurate force and impedance control is critical. His innovative use of neural networks to model complex robotic dynamics positions him as an emerging voice in the robotics community, with potential for high-impact applications in manufacturing and assistive robotics.
Research Focus
Key Achievements
Top Papers
- 1