Marlene Wessels
Papers
1
Total Citations
2
H-Index
1
About
Marlene Wessels is a pioneering researcher at the intersection of human-robot interaction (HRI) and explainable artificial intelligence (XAI), with a focus on making autonomous systems more transparent and trustworthy. Her work centers on developing immersive visualization techniques that bridge the gap between complex neural network policies and human understanding. In her highly cited 2025 paper, "Immersive Explainability: Visualizing Robot Navigation Decisions through XAI Semantic Scene Projections in Virtual Reality," Wessels introduced a novel approach to demystifying end-to-end robot policies trained via reinforcement learning. By projecting semantic scene interpretations into virtual reality, she enables humans to intuitively grasp why a robot makes specific navigation decisions—addressing the critical challenge of black-box reasoning in autonomous systems. This work has already garnered attention for its potential to enhance safety and collaboration in real-world robotics applications. Wessels’ contributions are particularly significant for advancing human-robot collaboration in dynamic environments, where trust and predictability are paramount. Her innovative fusion of XAI, VR, and robotics positions her as a leading voice in creating more interpretable and user-friendly autonomous systems.
Research Focus
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Top Papers
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