Khaled Saleh
Papers
2
Total Citations
12
H-Index
2
About
Khaled Saleh is a researcher at the forefront of human-robot interaction (HRI) and autonomous mobile robotics, with a focus on leveraging deep learning to bridge the gap between perception and action. His work centers on two key areas: inferring latent human behaviors and enabling intelligent robot navigation. In his highly cited 2021 paper, "Improving Users Engagement Detection using End-to-End Spatio-Temporal Convolutional Neural Networks," Saleh tackles the challenging task of automatically detecting user engagement during social robot interactions. By employing spatio-temporal CNNs, he demonstrated a data-driven method to understand complex human cues, a critical step for creating more responsive and natural social robots. Complementing this, his 2018 work, "Local Motion Planning for Ground Mobile Robots via Deep Imitation Learning," addresses autonomous navigation. Here, Saleh pioneered a novel approach that uses imitation learning from expert demonstrations and a simple monocular camera to teach robots local motion planning, effectively enabling them to learn complex driving behaviors without hand-coded rules. With a combined citation count of 12 from these foundational papers, Saleh's contributions are shaping a future where robots can both understand us and move safely through our world.
Research Focus
Key Achievements
Top Papers
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- 2