Max Robotics — Global Service Robot Directory + US Certification

About

Mohamed Cheriet is a prominent researcher at the intersection of artificial intelligence, robotics, and next-generation wireless communications. His work focuses on applying advanced machine learning techniques — particularly deep reinforcement learning and federated learning — to solve complex optimization problems in autonomous robotic systems, Industrial Internet of Things (IIoT), and 5G networks. Cheriet's most influential contribution, "Federated Deep Reinforcement Learning for Task Scheduling in Heterogeneous Autonomous Robotic Systems" (2022), has garnered 56 citations and addresses a pressing challenge in smart logistics: efficiently coordinating heterogeneous robot fleets in automated warehouses. By framing task scheduling as a queueing control optimization problem, his work offers scalable, privacy-preserving solutions with real-world applicability. Beyond warehouse automation, Cheriet has made notable contributions to 5G-enabled mission-critical robotics, exploring Ultra-Reliable Low-Latency Communication (URLLC) for swarm control, UAV-assisted cloud robotics for surveillance and rescue, and AI-powered digital twins for real-time industrial automation. His 2025 work integrating kinetic robot models with digital twin synchronization reflects his forward-looking research vision. With a growing body of work bridging game theory, federated learning, and autonomous systems, Cheriet is establishing himself as an important voice in the future of intelligent industrial automation.