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About

Charlotte Sennersten is a pioneering researcher at the intersection of cognitive robotics, spatial understanding, and deep learning for structural health monitoring. Her work bridges the gap between artificial intelligence and physical world interaction, with a primary focus on enabling robots to autonomously perceive, comprehend, and act within unstructured environments. Sennersten’s most cited work, *PointCrack3D* (2021, 6 citations), introduces a novel 3D-point-cloud-based deep neural network for detecting surface cracks in challenging settings like underground mine tunnels and natural rock faces—a critical contribution to infrastructure safety and risk management. Earlier foundational research, including *Towards a Cloud-Based Architecture for 3D Object Comprehension in Cognitive Robotics* (2014, 3 citations) and *Modelling Spatial Understanding* (2014, 2 citations), developed the CogOnto model, which grounds cognitive computing systems in heterogeneous sensor data to enable spatial awareness. Her innovative *REACT-R* framework (2016, 2 citations) extends the ACT-R cognitive architecture to incorporate robot embodiment, allowing autonomous adaptive behavior through direct sensorimotor interaction. Sennersten’s work is notable for its interdisciplinary approach, merging cognitive science, robotics, and computer vision to create systems that can learn, reason, and act safely in complex, real-world environments.