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In this paper, we propose the quantitative solution to figure out task execution time.
Figure 7 shows the result between the total task execution time and N w.
The uncertainty of the task execution time is also considered.
Because of the increasing complexity of maintenance tasks, it is not sufficient to train the technicians in task execution.
In this paper we present a behaviour-based mobile robot system for task execution.
A service framework is also introduced for the coordinated task execution by a humanoid robot.
For efficient task execution, environment knowledge is maintained in the memory.
Task execution in multi-agent environments may require cooperation among agents.
Neuromodulation was delivered in two conditions: online (a-tDCS during task execution) and offline (a-tDCS before task execution).
Therefore, working memory drives efficient inhibition by sustaining task instructions and appropriate responses throughout task execution.
This paper presents an attempt to use Bayesian Networks as a learning technique to manage task execution in mobile robotics.
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