Using Visual Attention to Evaluate Collaborative Control Architectures for Human Robot Interaction

Abstract

Abstract. Collaborative control architectures assist human users in performing tasks, without undermining their capabilities or curtailing the natural development of their skills. In this study, we evaluate our collaborative control architecture by investigating the visual attention patterns of robotic wheelchair users. Our initial hypothesis stated that the user would require less visual attention for driving, whilst they are being assisted by the collaborative system, thus allowing them to concentrate on higher level cognitive tasks, such as planning. However, our analysis of eye gaze patterns—as recorded by a head mounted eye tracking system—supports the opposite conclusion: that patterns of saccadic activation increase and become more chaotic under the assisted mode. Our findings highlight the necessity for techniques that assist the user in forming an appropriate mental model of the collaborative control architecture. ronmental exploration, or planning future manoeuvres. We also expect the driver to fixate on objects of interest, which may help to strengthen our intent-prediction system. We do not treat eye gaze as an active input device, in which the user tries to control the wheelchair by moving their head and/or eyes, as was demonstrated in [9]. Instead, we aim to use it as a passive device, to non-intrusively increase the user state vector (the knowledge we possess about the user at each time step). In this exploratory study, we observe the characteristics of the user’s eye movements, whilst performing typical manoeuvres, such as driving around offices and passing through narrow doorways. The observations are made over one independent variable, which can take one of two states: provide adaptive assistance, or provide no assistance. 1