Modelling Empathy in Social Robotic Companions

Leite, Iolanda; Pereira, Ana Isabel; Castellano, Ginevra; Mascarenhas, Samuel; Martinho, Carlos; Paiva, Ana

doi:10.1007/978-3-642-28509-7_14

Cited by 59 publications

(56 citation statements)

References 30 publications

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“…To address this issue, the action selection mechanism uses a reinforcement learning algorithm [2]. This way, the robot can adapt its empathic behaviour by trial and error considering the previous reactions of a particular user to an empathic strategy (for more details on the adaptive algorithm see [20]). Thus, we are investigating not only whether the introduction of empathic behaviours improves the interaction, but also if the adaptation of the robot's empathic behaviour over time plays an important role as well.…”

Section: Empathic Strategiesmentioning

confidence: 99%

Modelling empathic behaviour in a robotic game companion for children

Leite

Castellano

Pereira

et al. 2012

Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction

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The idea of autonomous social robots capable of assisting us in our daily lives is becoming more real every day. However, there are still many open issues regarding the social capabilities that those robots should have in order to make daily interactions with humans more natural. For example, the role of affective interactions is still unclear. This paper presents an ethnographic study conducted in an elementary school where 40 children interacted with a social robot capable of recognising and responding empathically to some of the children's affective states. The findings suggest that the robot's empathic behaviour affected positively how children perceived the robot. However, the empathic behaviours should be selected carefully, under the risk of having the opposite effect. The target application scenario and the particular preferences of children seem to influence the "degree of empathy" that social robots should be endowed with.

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Section: Empathic Strategiesmentioning

confidence: 99%

Modelling empathic behaviour in a robotic game companion for children

Leite

Castellano

Pereira

et al. 2012

Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction

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“…Overemphatic multi-modal output may perform badly over the longterm just as failures in multi-modal perception may become more obtrusive. The mutual adaptation of long-term human-human interaction remains largely to be studied in human-robot interaction, though existing studies do already demonstrate its necessity if human engagement in the interaction is to be maintained [23].…”

Section: From Days To Monthsmentioning

confidence: 99%

Long-term socially perceptive and interactive robot companions

Aylett

Castellano

Raducanu

et al. 2011

Proceedings of the 13th International Conference on Multimodal Interfaces

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This paper gives a brief overview of the challenges for multimodel perception and generation applied to robot companions located in human social environments. It reviews the current position in both perception and generation and the immediate technical challenges and goes on to consider the extra issues raised by embodiment and social context. Finally, it briefly discusses the impact of systems that must function continually over months rather than just for a few hours.

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“…The social signals most broadly used in the literature to model and assess the social qualities of human-robot interaction are gaze [5], physical proximity [36,40], tactile contact [19,46], joint attention [5,32,48], mirroring [41,49], imitation [24,39,48], synchrony [5,25,45] and coordination [28] of behavior, empathic behavior [33], and body posture [10,47] and orientation [14]. Children varied enormously in the ways that they used these to indicate a positive connection with Robin.…”

Section: Robin Treated As a Social Agentmentioning

confidence: 99%

Making New “New AI” Friends: Designing a Social Robot for Diabetic Children from an Embodied AI Perspective

Cañamero

Lewis

2016

Int J of Soc Robotics

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Robin is a cognitively and motivationally autonomous affective robot toddler with "robot diabetes" that we have developed to support perceived self-efficacy and emotional wellbeing in children with diabetes. Robin provides children with positive mastery experiences of diabetes management in a playful but realistic and natural interaction context. Underlying the design of Robin is an "Embodied" (formerly also known as "New") Artificial Intelligence (AI) approach to robotics. In this paper we discuss the rationale behind the design of Robin to meet the needs of our intended end users (both children and medical staff), and how "New AI" provides a suitable approach to developing a friendly companion that fulfills the therapeutic and affective requirements of our end users beyond other approaches commonly used in assistive robotics and child-robot interaction. Finally, we discuss how our approach permitted our robot to interact with and provide suitable experiences of diabetes management to children with very different social interaction styles.

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Modelling Empathy in Social Robotic Companions

Cited by 59 publications

References 30 publications

Modelling empathic behaviour in a robotic game companion for children

Modelling empathic behaviour in a robotic game companion for children

Long-term socially perceptive and interactive robot companions

Making New “New AI” Friends: Designing a Social Robot for Diabetic Children from an Embodied AI Perspective

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