My research centres on Human-Robot Interaction (HRI) and, in particular, on the question of how robots can learn more effectively about people and their surroundings. During my PhD, I examined the ways perception and cognition are structured by what I term dispositif networks—interlocking social, technical and cultural arrangements that scaffold human learning. Translating this insight into robotics, I design learning algorithms that embed the same layered contextual cues, enabling robots to interpret human behaviour more naturally and to adapt in real-time.

Building on that theoretical foundation, my current projects tackle the practical cornerstones of HRI: intelligent path planning, robust navigation and context-aware interaction. By fusing dispositif-informed learning models with state-of-the-art motion-planning and localisation techniques, I aim to create robots that not only move efficiently through complex spaces but also anticipate human intentions and collaborate safely and intuitively.