Melis Kapotoglu
Papers
4
Total Citations
31
H-Index
3
About
Melis Kapotoglu is a researcher in cognitive robotics, focusing on how robots can achieve robust, self-aware task execution through continual learning from experience. Her work centers on developing systems that enable robots to interpret their environments, monitor their own actions, and learn from past failures to avoid future errors. In her most-cited paper, "Scene Interpretation for Self-Aware Cognitive Robots" (2014, 16 citations), she proposed a visual interpretation system that allows robots to maintain a consistent world model, a key component of her lifelong experimental learning framework. Her subsequent research, including "Robust task execution through experience-based guidance for cognitive robots" (2015, 6 citations) and "Robots Avoid Potential Failures through Experience-based Probabilistic Planning" (2015, 6 citations), introduced online learning-guided planning methods that help robots proactively avoid failure situations. Kapotoglu also contributed to action monitoring with a hybrid system that detects runtime inconsistencies (2014, 3 citations). Her work has laid important groundwork for creating more resilient, autonomous robots capable of learning from their own experiences.
Research Focus
Key Achievements
Top Papers
- 1Scene Interpretation for Self-Aware Cognitive Robots16 citations · 2014
- 2
- 3
- 4Action monitoring in cognitive robots3 citations · 2014