Recognizing Engagement Behaviors in Human-Robot Interaction

Based on analysis of human-human interactions, we have developed an initial model of engagement for human-robot interaction which includes the concept of connection events, consisting of: directed gaze, mutual facial gaze, conversational adjacency pairs, and backchannels. We implemented the model in the open source Robot Operating System and conducted a human-robot interaction experiment to evaluate it

The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

Does A Loss of Social Credibility Impact Robot Safety?

This position paper discusses the safety-related functions performed by assistive robots and explores the relationship between trust and effective safety risk mitigation. We identify a measure of the robot’s social effectiveness, termed social credibility, and present a discussion of how social credibility may be gained and lost. This paper’s contribution is the identification of a link between social credibility and safety-related performance. Accordingly, we draw on analyses of existing systems to demonstrate how an assistive robot’s safety-critical functionality can be impaired by a loss of social credibility. In addition, we present a discussion of some of the consequences of prioritising either safety-related functionality or social engagement. We propose the identification of a mixed-criticality scheduling algorithm in order to maximise both safety-related performance and social engagement

Human-Robot Collaboration as a new paradigm in circular economy for WEEE management

E-waste is a priority waste stream as identified by the European Commission due to fast technological changes and eagerness of consumers to acquire new products. The value chain of the Waste on Electric and Electronic Equipment (WEEE) has to face several challenges: the EU directives requesting collection targets for 2019–2022, the costs of disassembly processes which is highly dependent on the applied technology and type of discarded device, and the sale of the obtained components and/or raw materials, with market prices varying according to uncontrolled variables at world level. This paper presents a human-robot collaboration for a recycling process where tasks are opportunistically assigned to either a human-being or a robot depending on the condition of the discarded electronic device. This solution presents some important advantages; i.e. tedious and dangerous tasks are assigned to robots whereas more value-added tasks are allocated to humans, thus preserving jobs and increasing job satisfaction. Furthermore, first results from a prototype show greater productivity and profitable projected investment