Papers
1
Total Citations
7
H-Index
1
About
Hani Saleh is a leading researcher at the intersection of robotics, neuromorphic computing, and energy-efficient embedded systems. His work focuses on enabling intelligent robotic perception and control through novel computational paradigms, particularly hyperdimensional computing (HDC) and event-based vision. Saleh’s most-cited paper, “Efficient event-based robotic grasping perception using hyperdimensional computing” (2024, 7 citations), introduces a groundbreaking approach that leverages Dynamic and Active Vision Sensors (DAVIS) to infer object properties for robotic grasping. By replacing traditional deep neural networks with HDC, his method achieves high accuracy in grasping quality prediction while drastically reducing computational and energy demands—a critical advancement for real-time industrial applications. This work exemplifies his broader contributions to bridging bio-inspired computing with practical robotics, demonstrating how event-driven sensing and lightweight algorithms can overcome the limitations of conventional vision systems. Saleh’s research has significant implications for autonomous manufacturing, where efficient, low-power perception is essential. His achievements highlight a commitment to developing scalable, resource-aware solutions that push the boundaries of robotic autonomy and neuromorphic hardware integration.
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Top Papers
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