Non-Linguistic Utterances (NLUs) are essential parts of emotive exchanges, not only in human-human interactions but also in the context of human-robot interactions. This research aims to deepen our understanding of emotive sounds for the domain of human-robot exchanges. We investigated the connections between certain audio qualities and the perception of emotional arousal and pleasure, designing a novel mapping of musical and prosodic audio parameters to a dimensional model of emotion which allows a robot to express a range of emotions. To assess the emotive sounds, we conducted an end-user evaluation in which participants were asked to interpret the emotive NLUs conveyed by robots. In the evaluation we examined 4 archetypal emotions: excitement, contentment, sadness, and anger. We placed participants' responses within the pleasure-arousal affect grid to analyze the distinctiveness of the emotive sounds. The study revealed that participants consistently associated excited, sad and angry NLUs with significantly different emotional states but did not do so for content NLUs. These findings contribute valuable insights into how to design NLUs which can enhance the emotional depth of human-robot interactions, with potential applications across various domains.

Gatekeeping is one of the most important tasks in MSL, especially defending aerial shots. This paper proposes a goalkeeper strategy based on combining two image sources. The goalkeeper uses the Omni-vision system to adjust heading orientation of the Kinect sensor for capturing both depth and RGB images of the ball. The depth information is used to detect the spatial position of the ball and the RGB image ensures the validity of the recognition. Once opponents shoot, the proposed system uses the first few detected positions of the ball to predict the interception point by the goalkeeper based on the least square method. The accuracy and effectiveness of the proposed strategy are verified by experimentation.