Semab Neimat Khan

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About

Semab Neimat Khan is a robotics researcher whose work lies at the intersection of reinforcement learning and autonomous navigation, with a particular focus on bio-inspired systems. Her most cited work, "Motion Planning for a Snake Robot using Double Deep Q-Learning" (2021, 15 citations), tackles the long-standing challenge of controlling modular snake robots in unknown, complex environments. By proposing a model-free, double deep Q-learning framework, she demonstrated how deep reinforcement learning can effectively handle the intricate control demands of these highly articulated mechanisms. This contribution is significant for advancing the autonomy of snake robots in search-and-rescue, inspection, and exploration tasks where traditional path planning falls short. Khan’s research showcases a practical bridge between cutting-edge AI techniques and real-world robotic applications, offering a scalable approach to motion planning that does not rely on pre-mapped environments. Her work has been recognized for its novelty in addressing the complex control of modular robots, and it continues to inspire further studies in deep reinforcement learning for adaptive locomotion.