On the simulation of interactive non-verbal behaviour in virtual humans

Development of virtual humans has focused mainly in two broad areas - conversational agents and computer game characters. Computer game characters have traditionally been action-oriented - focused on the game-play - and conversational agents have been focused on sensible/intelligent conversation. While virtual humans have incorporated some form of non-verbal behaviour, this has been quite limited and more importantly not connected or connected very loosely with the behaviour of a real human interacting with the virtual human - due to a lack of sensor data and no system to respond to that data. The interactional aspect of non-verbal behaviour is highly important in human-human interactions and previous research has demonstrated that people treat media (and therefore virtual humans) as real people, and so interactive non-verbal behaviour is also important in the development of virtual humans. This paper presents the challenges in creating virtual humans that are non-verbally interactive and drawing corollaries with the development history of control systems in robotics presents some approaches to solving these challenges - specifically using behaviour based systems - and shows how an order of magnitude increase in response time of virtual humans in conversation can be obtained and that the development of rapidly responding non-verbal behaviours can start with just a few behaviours with more behaviours added without difficulty later in development

Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload

Powered wheelchair users often struggle to drive safely and effectively and in more critical cases can only get around when accompanied by an assistant. To address these issues, we propose a collaborative control mechanism that assists the user as and when they require help. The system uses a multiple–hypotheses method to predict the driver’s intentions and if necessary, adjusts the control signals to achieve the desired goal safely. The main emphasis of this paper is on a comprehensive evaluation, where we not only look at the system performance, but, perhaps more importantly, we characterise the user performance, in an experiment that combines eye–tracking with a secondary task. Without assistance, participants experienced multiple collisions whilst driving around the predefined route. Conversely, when they were assisted by the collaborative controller, not only did they drive more safely, but they were able to pay less attention to their driving, resulting in a reduced cognitive workload. We discuss the importance of these results and their implications for other applications of shared control, such as brain–machine interfaces, where it could be used to compensate for both the low frequency and the low resolution of the user input

Selecting Metrics to Evaluate Human Supervisory Control Applications

The goal of this research is to develop a methodology to select supervisory control metrics. This methodology is based on cost-benefit analyses and generic metric classes. In the context of this research, a metric class is defined as the set of metrics that quantify a certain aspect or component of a system. Generic metric classes are developed because metrics are mission-specific, but metric classes are generalizable across different missions. Cost-benefit analyses are utilized because each metric set has advantages, limitations, and costs, thus the added value of different sets for a given context can be calculated to select the set that maximizes value and minimizes costs. This report summarizes the findings of the first part of this research effort that has focused on developing a supervisory control metric taxonomy that defines generic metric classes and categorizes existing metrics. Future research will focus on applying cost benefit analysis methodologies to metric selection. Five main metric classes have been identified that apply to supervisory control teams composed of humans and autonomous platforms: mission effectiveness, autonomous platform behavior efficiency, human behavior efficiency, human behavior precursors, and collaborative metrics. Mission effectiveness measures how well the mission goals are achieved. Autonomous platform and human behavior efficiency measure the actions and decisions made by the humans and the automation that compose the team. Human behavior precursors measure human initial state, including certain attitudes and cognitive constructs that can be the cause of and drive a given behavior. Collaborative metrics address three different aspects of collaboration: collaboration between the human and the autonomous platform he is controlling, collaboration among humans that compose the team, and autonomous collaboration among platforms. These five metric classes have been populated with metrics and measuring techniques from the existing literature. Which specific metrics should be used to evaluate a system will depend on many factors, but as a rule-of-thumb, we propose that at a minimum, one metric from each class should be used to provide a multi-dimensional assessment of the human-automation team. To determine what the impact on our research has been by not following such a principled approach, we evaluated recent large-scale supervisory control experiments conducted in the MIT Humans and Automation Laboratory. The results show that prior to adapting this metric classification approach, we were fairly consistent in measuring mission effectiveness and human behavior through such metrics as reaction times and decision accuracies. However, despite our supervisory control focus, we were remiss in gathering attention allocation metrics and collaboration metrics, and we often gathered too many correlated metrics that were redundant and wasteful. This meta-analysis of our experimental shortcomings reflect those in the general research population in that we tended to gravitate to popular metrics that are relatively easy to gather, without a clear understanding of exactly what aspect of the systems we were measuring and how the various metrics informed an overall research question