Yassine Chaibi

Papers

Total Citations

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About

Yassine Chaibi is a rising researcher at the forefront of intelligent robotics, whose work bridges the critical gap between simulation and real-world autonomy. His primary research areas focus on robotic manipulation, reinforcement learning, and the development of high-fidelity digital twins for humanoid robots. Chaibi’s most significant contribution is a comprehensive review of reinforcement learning for robotic grasping, which analyzes over 100 papers to provide actionable recommendations for integrating Deep Neural Networks and RL into physical systems—a work that has already garnered 13 citations. He further pushes the boundaries of embodied AI by creating an open-source digital twin of the Pepper robot using ROS 2, enabling researchers to train complex machine learning models in realistic simulations before deployment. This work, with 5 citations, represents a pivotal step toward unlocking advanced humanoid capabilities. Additionally, his overview of bio-inspired algorithms in robotics (2 citations) showcases his versatility in drawing from nature to solve engineering challenges. Chaibi’s research is essential reading for students and engineers aiming to bridge simulation and reality in autonomous systems.