Irene-Maria Tabakis
Papers
1
Total Citations
10
H-Index
1
About
Irene-Maria Tabakis is at the forefront of intelligent robotics and autonomous navigation, with a primary focus on path planning in complex, dynamic environments. Her most cited work, "Deep Reinforcement Learning-Based Path Planning for Dynamic and Heterogeneous Environments" (2024, 10 citations), pioneers the application of advanced DRL algorithms—specifically Double Deep Q-Network (DDQN) and Deep Deterministic Policy Gradient (DDPG)—to overcome the constraints of conventional path planning methods. By addressing real-world challenges such as moving obstacles and heterogeneous terrains, Tabakis has significantly advanced the adaptability and safety of autonomous systems. Her research bridges the gap between theoretical reinforcement learning and practical robotic deployment, offering scalable solutions for applications ranging from warehouse logistics to search-and-rescue missions. With a growing citation impact, Tabakis is recognized for integrating cutting-edge AI with robotics, and her work continues to inspire new directions in adaptive, learning-based navigation. Her contributions are particularly notable for their emphasis on real-time decision-making under uncertainty, marking her as a rising leader in the field of intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1