![]() Consequently, mechanisms that select the most salient stimuli should play an essential role in HRI however, these selection mechanisms are often omitted. Detectors are associated with the number of stimuli and the quality and delay of the detections. Therefore, to create an agile interaction with a high level of detail, a compromise is required regarding the number of detectors that can run simultaneously, in most cases. In some cases, this interaction encounters constraints from the perception capabilities of the robot, especially considering the computational resources that are needed by state-of-the-art perception techniques, such as those that are based on deep learning. In human–robot interaction (HRI), it is essential to select the most relevant stimuli to achieve a natural experience. Finally, this paper also presents the preliminary test results from the integration of this architecture into a real social robot. Furthermore, the attention-based architecture uses information from the robot’s decision-making system to produce user responses and robot decisions. We also defined mechanisms to establish the most salient stimulus for the detection or task in question. In this paper, the architecture is defined at a theoretical level to provide insights into the underlying bio-inspired mechanisms and at a practical level to integrate and test the architecture within the complete architecture of a robot. The novel contribution of this paper is the development of a perception architecture, which was based on the bio-inspired concept of endogenous attention being integrated into a real social robot. Moreover, this information allows the robot to react to both external stimuli and user responses. ![]() An essential capability of these systems is to provide a rich representation of the robot’s environment, typically using multiple sensory sources. A robust perception system is crucial for natural human–robot interaction.
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