A study was conducted to examine the expectations that younger and older individuals have about domestic robots and how these expectations relate to robot acceptance. In a questionnaire participants were asked to imagine a robot in their home and to indicate how much items representing technology, social partner, and teammate acceptance matched their robot. There were additional questions about how useful and easy to use they thought their robot would be. The dependent variables were attitudinal and intentional acceptance. The analysis of the responses of 117 older adults (aged 65–86) and 60 younger adults (aged 18–25) indicated that individuals thought of robots foremost as performance-directed machines, less so as social devices, and least as unproductive entities. The robustness of the Technology Acceptance Model to robot acceptance was supported. Technology experience accounted for the variance in robot acceptance due to age.

Many companies are developing robots for the home, including robots specifically for older adults. There is little understanding, however, about the types and characteristics of tasks that younger and older individuals would be willing to let a robot perform. In a mailed questionnaire, participants were asked to indicate their willingness to have a robot perform each of 15 robot tasks that required different levels of interaction with the human owner and different levels of task criticality. The responses of 117 older adults (aged 65–86) and 60 younger adults (aged 18–25) were analyzed. The results indicated that respondents of both groups were more willing to have robots perform infrequent, albeit important, tasks that required little interaction with the human compared to service-type tasks with more required interaction; they were least willing to have a robot perform non-critical tasks requiring extensive interaction between robot and human. Older adults reported more willingness than younger adults in having a robot perform critical tasks in their home. The results suggest that both younger and older individuals are more interested in the benefits that a robot can provide than in their interactive abilities.

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Designers of decision aids should consider explicitly stating costs associated with reliance on the aid, as individuals may differ in how they interpret and respond to changing costs.

Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator’s cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI2). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator’s states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI2 (CHMI2) implementations. The key neurophysiological measurements used in this context and their relationship with the operator’s cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI2 applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.