In a growing number of application domains, human decision-making is being supported by automated systems. While previous research has focused extensively on the negative consequences of automation support in terms of an overuse of such systems, we argue that this focus has largely overlooked another crucial issue: Humans often deteriorate the performance of automation. Specifically, human–automation dyads commonly perform worse than the system alone because humans, in an attempt to improve decisions, unfortunately interfere with correct system recommendations. This problem will only grow as systems based on artificial intelligence (AI) become more reliable and the gap between human-only and system-only performance continues to widen. We therefore outline the need for research that addresses this persisting and increasingly relevant issue. One approach to counteract this problem is to make systems more transparent and give humans more information on the system. However, while numerous explainability approaches have been brought forward, only very few show convincing effects. To be truly useful, we argue that systems need to be explainable in terms of effective behavioral guidance. Furthermore, beyond just thinking about how to enable humans to better adapt to the system (as is the case with explainability approaches), systems should be more human-centric, taking into account human strengths and weaknesses, and ultimately adapting to humans to enable synergy between humans and AI.

Anthropomorphism, as a design feature of robots, is widely applied to enhance human-robot interaction in the social domain. Current knowledge of how anthropomorphism influences perception, attitudes, and actual behavior is gained via research with depicted and embodied robots. However, it remains unclear how comparable gained insights of both approaches are. Results of a current meta-analysis suggest that anthropomorphism positively influences subjective and objective measures in case of embodied robots, whereas in case of depicted robots predominantly effects on subjective measures seem to emerge. This follow-up analysis aims to further investigate this difference by using a recoded dataset including data of 41 studies, involving over 3,000 participants. The results illustrate that anthropomorphism investigated via embodied robots indeed facilitates both subjective and objective outcomes. Remarkably, studies concerning effects of anthropomorphism using depicted robots showed positive effects on a subjective level but failed to show any effects on an objective level. In conclusion, the results show that the consequences of anthropomorphism in human-robot interaction depend on how robots are presented to participants. Moreover, they reveal that the transfer of results gained via depicted robots to embodied human-robot interaction might lead to both overestimation on a subjective level and underestimation on an objective level regarding the consequences of anthropomorphism.

This article describes a flight simulator study to validate a new command control system for longitudinal load factor nx. The system is called nxControl and actuates thrust, speedbrakes, and wheel brakes that directly influence the total energy state of the aircraft. It aims at manually flying with higher precision and lower workload. After a brief overview of the functionality of nxControl and its cockpit interfaces, the key results of earlier proof-of-concept simulator studies are summarized. The focus lies on the comprehensive flight simulator study with 24 airline pilots to conclusively evaluate the complete system. The task was a highly demanding approach trajectory containing segmented-continuous-descent in gusty wind conditions, touch-down, deceleration, and taxi as well as engine failure at low altitude. With nxControl, the pilots achieved higher precision in airspeed and energy control with lower workload compared to conventional manual thrust control. In addition, nxControl achieved safety benefits in case of an engine failure.

Within the scope of the current research the goal was to develop an autonomous transport assistant for hospitals. As a sort of social robots, they need to fulfill two main requirements with respect to their interactive behavior with humans: (1) a high level of safety and (2) a behavior that is perceived as socially proper. One important element includes the characteristics of movement. However, state-of-the-art hospital robots rather focus on safe but not smart maneuvering. Vital motion parameters in human everyday environment are personal space and velocity. The relevance of these parameters has also been reported in existing human-robot interaction research. However, to date, no minimal accepted frontal and lateral distances for human-mechanoid proxemics have been explored. The present work attempts to gain insights into a potential threshold of comfort and additionally, aims to explore a potential interaction of this threshold and the mechanoid's velocity. Therefore, a user study putting the users in control of the mechanoid was conducted in a laboratory hallway-like setting. Findings align with previously reported personal space zones in human-robot interaction research. Minimal accepted frontal and lateral distances were obtained. Furthermore, insights into a potential categorization of the lateral personal space area around a human are discussed for human-robot interaction.