Melis Kapotoglu

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Cognitive robots need to detect execution failures in runtime to prevent potential damages to their environments or objects in their interest. For this reason, robots monitor the execution to detect any inconsistencies. In this paper, we propose a hybrid monitoring system that processes different sensory data from different sources by using both model-based(More)
Robots should avoid potential failure situations to safely execute their actions and to improve their performances. For this purpose, they need to build and use their experience online. We propose online learning-guided planning methods to address this problem. Our method includes an experiential learning process using Inductive Logic Programming (ILP) and(More)
Robustness in task execution requires tight integration of continual planning, monitoring, reasoning and learning processes. In this paper, we investigate how robustness can be ensured by learning from experience. Our approach is based on a learning guided planning process for a robot that gains its experience from action execution failures through lifelong(More)
A cognitive robot may face several types of failures during the execution of its actions in the physical world. In this paper, we investigate how robots can ensure robustness by gaining experience on action executions, and we propose a lifelong experimental learning method to derive new hypotheses. Our proposed learning process takes into account the(More)
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