Successful social robot services depend on how robots can interact with users. The effective service can be obtained through smooth, engaged, and humanoid interactions in which robots react properly to a user’s affective state. This article proposes a novel Automatic Cognitive Empathy Model, ACEM, for humanoid robots to achieve longer and more engaged human-robot interactions (HRI) by considering humans’ emotions and replying to them appropriately. The proposed model continuously detects the affective states of a user based on facial expressions and generates desired, either parallel or reactive, empathic behaviors that are already adapted to the user’s personality. Users’ affective states are detected using a stacked autoencoder network that is trained and tested on the RAVDESS dataset. The overall proposed empathic model is verified throughout an experiment, where different emotions are triggered in participants and then empathic behaviors are applied based on proposed hypothesis. The results confirm the effectiveness of the proposed model in terms of related social and friendship concepts that participants perceived during interaction with the robot.

The Social Robots in Therapy workshop series aims at advancing research topics related to the use of robots in the contexts of Social Care and Robot-Assisted Therapy (RAT). Robots in social care and therapy have been a long time promise in HRI as they have the opportunity to improve patients life significantly. Multiple challenges have to be addressed for this, such as building platforms that work in proximity with patients, therapists and health-care professionals; understanding user needs; developing adaptive and autonomous robot interactions; and addressing ethical questions regarding the use of robots with a vulnerable population. The full-day workshop follows last year's edition which centered on how social robots can improve health-care interventions, how increasing the degree of autonomy of the robots might affect therapies, and how to overcome the ethical challenges inherent to the use of robot assisted technologies. This 2ndedition of the workshop will be focused on the importance of equipping social robots with socio-emotional intelligence and the ability to perform meaningful and personalized interactions. This workshop aims to bring together researchers and industry experts in the fields of Human-Robot Interaction, Machine Learning and Robots in Health and Social Care. It will be an opportunity for all to share and discuss ideas, strategies and findings to guide the design and development of robot-assisted systems for therapy and social care implementations that can provide personalize, natural, engaging and autonomous interactions with patients (and health-care providers).

This work presents preliminary findings from a pilot study researching the complementary and similarity effect of personality of robots on children ( N=22). We aim to advance research on robot personality by using a pairwise robot comparison study, a novel method in HRI. Our findings show that the attributed gender and the voice of the robots were the main reasons given for a preference. Based on the preliminary data we hypothesize that boys have a preference for the robot with the same gender as themselves.

We present an affective model for an autonomous decision agent implementable within non-expensive robotic platforms. The agent makes decisions influenced by affective factors when interacting with humans and other agents under the Adversarial Risk Analysis framework. The model is based on a multi-objective additive utility function, with weights partly regulated by the agent's emotional states and mood. Our motivation for this work is not to provide a more accurate descriptive model, but rather to use affection as a basic element within a decision-making process that could lead to improved performance of a robotic agent, possibly making it more attractive. This would facilitate its adoption in application areas such as companion pets to provide emotional support to the elderly or kids with autism, or as educational support within an edutainment framework.

We describe how adversarial risk analysis may be used to support the decision making of an autonomous agent that needs to interact with other agents and persons within a competitive environment. We propose several contextualizations for the problem and suggest the conceptual solution in the proposed scenarios.