Mario Srouji
Papers
2
Total Citations
9
H-Index
2
About
Mario Srouji is a robotics researcher focused on safe autonomous navigation and motion planning, with particular emphasis on collision avoidance for mobile robots and humanoid platforms. His work bridges reinforcement learning and perception systems to enable robots to operate safely in complex, real-world environments. Srouji’s most cited paper, “SAFER: Safe Collision Avoidance Using Focused and Efficient Trajectory Search with Reinforcement Learning” (2023, 6 citations), introduces an efficient system that corrects operator control commands in real-time to enhance safety, combining reinforcement learning with trajectory optimization. Building on this, his 2024 work “ARMOR: Egocentric Perception for Humanoid Robot Collision Avoidance and Motion Planning” (3 citations) addresses critical sensing gaps in humanoid robots by developing a novel egocentric perception system that integrates wearable-like depth sensors with software for improved motion planning in dense environments. Though early in his career, Srouji’s contributions are already shaping how robots perceive and navigate their surroundings, with potential applications in assistive robotics, industrial automation, and human-robot interaction. His research demonstrates a commitment to practical, deployable solutions that prioritize safety and real-world robustness.
Research Focus
Key Achievements
Top Papers
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