The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios

Churamani, Nikhil; Anton, Paul; Brugger, Marc; Fliesswasser, Erik; Hummel, Thomas; Mayer, Julius; Mustafa, Waleed; Ng, Hwei Geok; Nguyen, Thi Linh Chi; Nguyen, Quan; Soll, Marcus; Springenberg, Sebastian; Griffiths, Sascha; Heinrich, S.; Navarro-Guerrero, Nicolás; Strahl, Erik; Twiefel, Johannes; Weber, Cornelius; Wermter, Stefan

doi:10.1145/3125739.3125756

Cited by 58 publications

(26 citation statements)

References 28 publications

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“…On the one hand, the big data was investigated in different contexts: the behavioral intention of big data analytics [16]; the use of big data to obtain online consumer reviews (OCR), which will benefit companies and consumers in an e-commerce context [17]; big data technology adoption [18,19]; and the effect of resistance to the use of big data techniques in companies [20]. On the other hand, research into AI applications and implementation only exists in specific sectors, such as education [21], health [22], consumer privacy [23], and social networks [24].…”

Section: Introductionmentioning

confidence: 99%

Online Recommendation Systems: Factors Influencing Use in E-Commerce

et al. 2020

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The increasing use of artificial intelligence (AI) to understand purchasing behavior has led to the development of recommendation systems in e-commerce platforms used as an influential element in the purchase decision process. This paper intends to ascertain what factors affect consumers’ adoption and use of online purchases recommendation systems. In order to achieve this objective, the Unified Theory of Adoption and Use of Technology (UTAUT 2) is extended with two variables that act as an inhibiting or positive influence on intention to use: technology fear and trust. The structural model was assessed using partial least squares (PLS) with an adequate global adjustment on a sample of 448 users of online recommendation systems. Among the results, it’s highlighted the importance of the inhibiting role of technology fear and the importance that users attach to the level of perceived trust in the recommendation system are highlighted. The performance expectancy and hedonic motivations have the greatest influence on intention to use these systems. Based on the results, this work provides a relevant recommendation to companies for the design of their e-commerce platforms and the implementation of online purchase recommendation systems.

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Section: Introductionmentioning

confidence: 99%

Online Recommendation Systems: Factors Influencing Use in E-Commerce

et al. 2020

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“…These aspects are inter-dependent: physical adaption addresses user-specific behaviors, cognitive adaption addresses users' intention of such behaviors, and social adaption addresses the relationship between a user and a robot, which changes over physical and cognitive adaption. Personalization can happen both in a passive manner where a robot learns by observation (e.g., Kato et al [82]) or in a proactive manner where a robot engages the user in teaching it information required for personalization, i.e., a teachable robot (e.g., Churamani et al [25]). Personalized and adaptive HRI is still in its infancy, especially for longitudinal HRI [74].…”

Section: Misunderstanding the Usermentioning

confidence: 99%

A Taxonomy of Social Errors in Human-Robot Interaction

Tian

OviattSharon

2021

J. Hum.-Robot Interact.

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Robotic applications have entered various aspects of our lives, such as health care and educational services. In such Human-robot Interaction (HRI), trust and mutual adaption are established and maintained through a positive social relationship between a user and a robot. This social relationship relies on the perceived competence of a robot on the social-emotional dimension. However, because of technical limitations and user heterogeneity, current HRI is far from error-free, especially when a system leaves controlled lab environments and is applied to in-the-wild conditions. Errors in HRI may either degrade a user’s perception of a robot’s capability in achieving a task (defined as performance errors in this work) or degrade a user’s perception of a robot’s socio-affective competence (defined as social errors in this work). The impact of these errors and effective strategies to handle such an impact remains an open question. We focus on social errors in HRI in this work. In particular, we identify the major attributes of perceived socio-affective competence by reviewing human social interaction studies and HRI error studies. This motivates us to propose a taxonomy of social errors in HRI. We then discuss the impact of social errors situated in three representative HRI scenarios. This article provides foundations for a systematic analysis of the social-emotional dimension of HRI. The proposed taxonomy of social errors encourages the development of user-centered HRI systems, designed to offer positive and adaptive interaction experiences and improved interaction outcomes.

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“…For example, Lemaignan et al ( 2017 ) proposed a practical implementation for social human-robot interaction combining geometric reasoning, situation assessment, knowledge acquisition, and representation of multiple agents, for human-aware task planning. Churamani et al ( 2017 ) built a human-robot interaction module to engage personalized conversations in order to teach robots to recognize different objects. Devin and Alami ( 2016 ) developed a framework which allows robots to estimate other agents' mental states e.g., goals, plans and actions and take them into account when executing human-robot shared plans.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Despite the significant resources devoted in sHRI, the majority of existing systems consider mainly the spatial aspects of the world without encapsulating the concept of the time dimension. As a result, contemporary research has largely concentrated on enhancing robotic sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction between agents (Das et al, 2015 ; Baraglia et al, 2016 ; Devin and Alami, 2016 ; Churamani et al, 2017 ). Still, human-machine confluence encompasses inherent temporal aspects that are often considered only implicitly in robotic applications, with clear negative effects regarding the integration of artificial agents into human environments.…”

Section: Introductionmentioning

confidence: 99%

Time-Aware Multi-Agent Symbiosis

et al. 2020

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Contemporary research in human-machine symbiosis has mainly concentrated on enhancing relevant sensory, perceptual, and motor capacities, assuming short-term and nearly momentary interaction sessions. Still, human-machine confluence encompasses an inherent temporal dimension that is typically overlooked. The present work shifts the focus on the temporal and long-lasting aspects of symbiotic human-robot interaction (sHRI). We explore the integration of three time-aware modules, each one focusing on a diverse part of the sHRI timeline. Specifically, the Episodic Memory considers past experiences, the Generative Time Models estimate the progress of ongoing activities, and the Daisy Planner devices plans for the timely accomplishment of goals. The integrated system is employed to coordinate the activities of a multi-agent team. Accordingly, the proposed system (i) predicts human preferences based on past experience, (ii) estimates performance profile and task completion time, by monitoring human activity, and (iii) dynamically adapts multi-agent activity plans to changes in expectation and Human-Robot Interaction (HRI) performance. The system is deployed and extensively assessed in real-world and simulated environments. The obtained results suggest that building upon the unfolding and the temporal properties of team tasks can significantly enhance the fluency of sHRI.

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The Impact of Personalisation on Human-Robot Interaction in Learning Scenarios

Cited by 58 publications

References 28 publications

Online Recommendation Systems: Factors Influencing Use in E-Commerce

Online Recommendation Systems: Factors Influencing Use in E-Commerce

A Taxonomy of Social Errors in Human-Robot Interaction

Time-Aware Multi-Agent Symbiosis

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