Socially-Sensitive Technologies for HRI: Challenges and Perspectives (original) (raw)
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Coordination of human-robot joint activity must depend on the ability of human and artificial agencies to interpret and interleave their actions. In this paper we consider the potential of artificial emotions to serve as task-relevant coordination devices in human-robot teams. We present two studies aiming to understand whether a non-humanoid robot can express artificial emotions in a manner that is meaningful to a human observer, the first based on static images and the second on the dynamic production of embodied robot expressions. We present a mixed-methods approach to the problem, combining statistical treatment of ratings data and thematic analysis of qualitative data. Our results demonstrate that even very simple movements of a non-humanoid robot can convey emotional meaning, and that when people attribute emotional states to a robot, they typically apply an event-based frame to make sense of the robotic expressions they have seen. Artificial emotions with high arousal level and negative valence are relatively easy for people to recognise compared to expressions with positive valence. We discuss the potential for using motion in different parts of a non-humanoid robot body to support the attribution of emotion in HRI, towards ethically responsible design of artificial emotions that could contribute to the efficacy of joint human-robot activities.
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Robots and agents are becoming increasingly prominent in everyday life, taking on a variety of roles, including helpers, coaches, and even social companions. A core requirement for these social agents is the ability to establish and maintain long-term trusting and engaging relationship with their human users. Much research has already been done on the prerequisites for these types of social agents and robots, in affective computing, social computing and affective HCI. A number of disciplines within psychology and the social sciences are also relevant, contributing theories, data and methods relevant for the emerging areas of social robotics, and social computing in general. However, the complexity of the task of designing these social agents, and the diversity of the relevant disciplines, can be overwhelming. This paper presents a summary of a special session at ACII 2009 whose purpose was to provide an overview of the state-of-the-art in social agents and robots, and to explore som...
Artificial Emotions to Assist Social Coordination in HRI
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Human-Robot Interaction requires coordination strategies that allow human and artificial agencies to interpret and interleave their actions. In this paper we consider the potential of artificial emotions to serve as coordination devices in human-robot teams. We propose an approach for modelling action selection based on artificial emotions and signalling a robot’s internal state to human team member. We describe an architecture that drives the display of artificial emotional gestures with a model of latched internal emotional states. We also present preliminary data on human recognition rates for a candidate set of artificial emotional expressions in a Lego robot.
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Proc. SPIE 9084, Unmanned Systems Technology XVI, 2014
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A Review on The Development and Effect of Conversational Agents and Social Robots
HCI addresses to the concept of Human-Machine communication through including but not limited to AI-enabled embodied conversational software agents. The emergence of these agents has changed the history of computing and robotics once and for all. One of the most prominent social and intellectual qualities in humans is the ability to have conversations. Typically, a conversation takes place between people through verbal and non-verbal mediums. Languages play a vital role in these communications and conversations. Humanness and human-like interaction qualities are found to be in the core of the human-computer interface designs from the beginning of this research doctrine [1]. Programming languages has enabled computer scientists to establish a connection between humans and machines that enables the machine to understand the instructions given. However, the widespread use of cell phones, computers and other smart gadgets has clearly made it a demand of time that the machines used today can understand the commands given in natural languages (i.e. English, German, Spanish, etc.) as the user set is not limited to the computer scientists anymore[1]. Hence, robotics, natural language processing, machine learning, artificial intelligence, etc. has combined force to bridge the communication gap between the machines and the users. From ELIZA [3], Rea [4] to Siri, Amazon Alexa or Google assistant, the software interfaces has come a long way through a lengthy development process. They have proven to have enough influence to change the social, economic and political outcomes through their intelligent behavior [2]. The boundary between human-like and bot-like behavior is greyer then it is black and white [2]. The software interfaces has changed their appearance over the time by stripping down from the ideals of face-to-face conversations. The chatbots (i.e. Twitter bots) found online has developed different social media ecosystems [2] where humans and robots interact with each other in the same plane. To have a conversation or interaction with the machines humans are being trained to accept and use a new set of vocabularies [1]. In this paper, I would like to discuss how these conversational agents and social robots are shaping our social media ecosystems. I will revisit the interrelation between humans and machines while focusing on the socio-cultural impact of these robots into our IoT –enabled smart homes and online virtual spaces.