Eva Artusi
Papers
2
Total Citations
3
H-Index
1
About
Dr. Eva Artusi is a rising researcher in autonomous systems and fault-tolerant control, with a specialized focus on enhancing the resilience of Autonomous Underwater Vehicles (AUVs). Her work centers on integrating deep reinforcement learning (DRL) into control reallocation strategies to manage actuator faults, a critical challenge for robots operating in hostile, deep-sea environments. In her 2025 paper, "Control Reallocation Using Deep Reinforcement Learning for Actuator Fault Recovery of an Autonomous Underwater Vehicle," she pioneers a DRL-based approach that dynamically redistributes control efforts to prevent stability loss and performance degradation during failures. Her subsequent work, "Sim-to-Real Transfer for AUV Fault Control with Deep Reinforcement Learning," addresses the pivotal gap between simulation and real-world deployment, leveraging open-access tools to train models that can be effectively transferred to physical systems. Though early in her career, with papers accumulating initial citations, Artusi’s contributions are already recognized for their practical impact on robotic safety and autonomy. Her methodology promises to extend mission longevity and reliability for AUVs in exploration, defense, and environmental monitoring, marking her as a promising innovator in intelligent control systems.
Research Focus
Key Achievements
Top Papers
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