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About

Jennifer Kwiatkowski is a leading researcher in robotic tactile sensing and dexterous manipulation, whose work bridges the gap between human-like touch perception and autonomous grasping systems. Her primary research areas include tactile sensor development, grasp stability prediction, and proprioceptive-tactile sensor fusion. Kwiatkowski’s most impactful contribution is her pioneering work on integrating proprioception and tactile signals using convolutional neural networks to assess grasp stability—a study that has garnered 55 citations and laid the foundation for more reliable robotic object handling. She has also advanced tactile sensor technology with her development of a capacitive tactile sensor employing mutual capacitance sensing, achieving enhanced resolution critical for complex environments. Her research on tactile-based grasp stability prediction has identified key limitations in current methods, while her exploration of object property determination during grasp failures offers novel strategies for robust regrasping. Additionally, her work on tactile-based object recognition using grasp-centric exploration demonstrates innovative approaches to reducing reliance on visual feedback. Through these contributions, Kwiatkowski has established herself as a key figure in advancing robotic touch capabilities, with her cumulative work informing both industrial automation and humanoid robotics.