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One of the fundamental prerequisites for effective collaborations between interactive partners is the mutual sharing of the attentional focus on the same perceptual events. This is referred to as joint attention. In psychological, cognitive, and social sciences, its defining elements have been widely pinpointed. Also the field of human-robot interaction has extensively exploited joint attention which has been identified as a fundamental prerequisite for proficient human-robot collaborations. However, joint attention between robots and human partners is often encoded in prefixed robot behaviours that do not fully address the dynamics of interactive scenarios. We provide autonomous attentional behaviour for robotics based on a multi-sensory perception that robustly relocates the focus of attention on the same targets the human partner attends. Further, we investigated how such joint attention between a human and a robot partner improved with a new biologically-inspired memory-based attention component. We assessed the model with the humanoid robot iCub involved in performing a joint task with a human partner in a real-world unstructured scenario. The model showed a robust performance on capturing the stimulation, making a localisation decision in the right time frame, and then executing the right action. We then compared the attention performance of the robot against the human performance when stimulated from the same source across different modalities (audio-visual and audio only). The comparison showed that the model is behaving with temporal dynamics compatible with those of humans. This provides an effective solution for memory-based joint attention in real-world unstructured environments. Further, we analyzed the localisation performances (reaction time and accuracy), the results showed that the robot performed better in an audio-visual condition than an audio only condition. The performance of the robot in the audio-visual condition was relatively comparable with the behaviour of the human participants whereas it was less efficient in audio-only localisation. After a detailed analysis of the internal components of the architecture, we conclude that the differences in performance are due to egonoise which significantly affects the audio-only localisation performance.

A Biological Inspired Cognitive Framework for Memory-Based Multi-Sensory Joint Attention in Human-Robot Interactive Tasks