Developing preferential attention to a speaker: a robot learning to recognise its carer

In this paper we present a socially interactive multi-modal robotic head, ERWIN - Emotional Robot With Intelligent Networks, capable of emotion expression and interaction via speech and vision. The model presented shows how a robot can learn to attend to the voice of a specific speaker, providing a relevant emotional expressive response based on previous interactions. We show three aspects of the system; first, the learning phase, allowing the robot to learn faces and voices from interaction. Second, recognition of the learnt faces and voices, and third, the emotion expression aspect of the system. We show this from the perspective of an adult and child interacting and playing a small game, much like an infant and caregiver situation. We also discuss the importance of speaker recognition in terms of human-robot-interaction and emotion, showing how the interaction process between a participant and ERWIN can allow the robot to prefer to attend to that person.</p

A neural network classifier for notch filter classification of sound-source elevation in a mobile robot

An important aspect of all robotic systems is sensing and there are many sensing modalities used including vision, tactile, olfactory and acoustics to name a few. This paper presents a robotic system for sensing in acoustics, specifically in elevation localization. The model presented is a two-stage model incorporating spectral analysis using artificial pinna and an artificial neural network for classification and elevation estimation. The spectral classifier uses notch filters to analyze changes in attenuation of certain frequencies with elevation. This paper shows how using the spectral output of a signal generated by an artificial pinna can be classified by a feed-forward backpropagation neural network to estimate the elevation of a sound-source.</p

A recurrent neural network for sound-source motion tracking and prediction

Recurrent neural networks (RNN) have been used in many applications for both pattern detection and prediction. This paper shows the use of RNN's as a speed classifier and predictor for a robotic sound source tracking system. The system requires extensive training to classify all possible speeds to enable dynamic tracking of the most prominent sound within the environment.</p

Towards a model of emotion expression in an interactive robot head

In this paper we present a robotic head designed for interaction with humans, endowed with mechanisms to make the robot respond to social interaction with emotional expressions, allowing the emotional expression of the robot to be directly influenced by the social interaction process. We look into how emotionally expressive visual feedback from the robot can enrich the interaction process and provide the participant with additional information regarding the interaction, allowing the user to better understand the intentions of the robot. We discuss some of the interactions that are possible with ERWIN and how this can effect the response of the system. We show experimental scenarios where the interaction processes influences the emotional expressions and how the participants interpret this. We draw our conclusions from the feedback from experiments, showing that indeed emotional expression can have an influence on the social interaction between a robot and human</p

Using robots to understand animal social cognition

In studies on animal cognition live animals are often used to model behaviours, but may not do so reliably. We have been using a robotic bearded dragon (Pogona vitticeps) to investigate social behaviours in a controlled way, providing new insights into reptile cognition.</p

Analysis of bat wing beat frequency using Fourier transform

Computer vision techniques have been used extensively to automatically monitor human activities; however, applications for analysing animal behaviour are sparse. The analysis of bat behaviour in particular has attracted only one or two studies. Most existing work uses either expensive thermal imaging equipment, or bespoke sensors which are not accessible to field researchers, ecologists, and scientists studying behaviour. The work we present here uses spectral analysis techniques to quantify wingbeat frequency, using a single imaging device in low-light. We propose two modified techniques based on bounded box metrics, and similarity matrices, for measuring periodic and cyclical motion as a 1D time domain signal. These are transformed to the frequency domain using Short Time Fourier Transform (STFT). Finally we evaluate these techniques against the baseline algorithm proposed by Cutler and Davis [5], using expert-annotated ground-truth data.</p