Michael Edegware
Papers
1
Total Citations
14
H-Index
1
About
Michael Edegware is a leading researcher in the field of robotic perception and manipulation, with a primary focus on haptic and tactile sensing. His work addresses a fundamental challenge in robotics: enabling machines to learn about object properties through non-visual sensory modalities, much like humans do. Edegware’s most notable contribution is his pioneering research on haptic knowledge transfer between heterogeneous robots, as demonstrated in his highly cited 2020 paper, "Haptic Knowledge Transfer Between Heterogeneous Robots using Kernel Manifold Alignment" (14 citations). This work introduced a novel framework that allows robots with different physical configurations to share learned tactile experiences—such as grasping, lifting, and pushing—by aligning high-dimensional sensory data across platforms. By leveraging kernel manifold alignment, Edegware’s approach significantly reduces the need for retraining from scratch, accelerating robotic learning and adaptability. His research has profound implications for autonomous systems in unstructured environments, from manufacturing to healthcare. With a growing citation impact, Edegware is recognized as a rising star in embodied AI, pushing the boundaries of how robots perceive and interact with the physical world through touch.
Research Focus
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Top Papers
- 1