Pascal Wiggers
Papers
2
Total Citations
19
H-Index
2
About
Pascal Wiggers is a researcher at the forefront of autonomous robotics, with a particular focus on humanoid navigation and adaptive learning in dynamic environments. His work centers on enabling robots, such as the humanoid Nao, to perceive, map, and move intelligently through real-world domestic spaces. Wiggers made a notable contribution to the field with his 2014 paper, "An Approach to Navigation for the Humanoid Robot Nao in Domestic Environments," which has garnered 14 citations for its practical framework in home robotics. In a 2012 study, he advanced machine learning by applying a modified Extended Classifier System (XCS) to the Nao platform, demonstrating how evolutionary search and reinforcement learning can allow a robot to build a complete, accurate, and maximally general map of its surroundings. This work, cited 5 times, highlights his skill in bridging theoretical learning models with real-time robotic behavior. Wiggers’ research is particularly valuable for students and engineers working on adaptive, human-centered robotics, as it provides a clear path from algorithm design to physical implementation in unpredictable settings.
Research Focus
Key Achievements
Top Papers
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