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Guaranteeing people&#39;s engagement during an interaction is very important to elicit positive and effective emotions in public service scenarios. A robot should be able to detect its interlocutor&#39;s level and mode of engagement to accordingly modulate its behaviours. 
However, there is not a generally accepted model to annotate and classify engagement during an interaction. In this work, we consider engagement as a multidimensional construct with three relevant dimensions: affective, cognitive and behavioural. To be automatically evaluated by a robot, such a complex construct requires the selection of the proper interaction features among a large set of possibilities. Moreover, manually collecting and annotating large datasets of real interactions are extremely time-consuming and costly. In this study, we collected the recordings of human-robot interactions in a bartending scenario, and we compared different feature selection and regression models to find the features that characterise a user&#39;s engagement in the interaction, and the model that can efficiently detect them. Results showed that the characterisation of each dimension separately in terms of features and regression obtains better results with respect to a model that directly combines the three dimensions.

IEEE RO-MAN 2021

Affective, Cognitive and Behavioural Engagement Detection for Human-Robot Interaction in a Bartending Scenario

affective robotics

social robotics

engagement detection

human-robot interaction

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

Guaranteeing people's engagement during an interaction is very important to elicit positive and effective emotions in public service scenarios. A robot should be able to detect its interlocutor's level and mode of engagement to accordingly modulate its behaviours. 
However, there is not a generally accepted model to annotate and classify engagement during an interaction. In this work, we consider engagement as a multidimensional construct with three relevant dimensions: affective, cognitive and behavioural. To be automatically evaluated by a robot, such a complex construct requires the selection of the proper interaction features among a large set of possibilities. Moreover, manually collecting and annotating large datasets of real interactions are extremely time-consuming and costly. In this study, we collected the recordings of human-robot interactions in a bartending scenario, and we compared different feature selection and regression models to find the features that characterise a user's engagement in the interaction, and the model that can efficiently detect them. Results showed that the characterisation of each dimension separately in terms of features and regression obtains better results with respect to a model that directly combines the three dimensions.

panel

1. Healthcare robotics are the future of elderly health care.
2. The users' age is an essential actor in the process.
3. We studied the effect of age on the users' perception o a care robot.
4. The results show a significant effect for age.

Robot Gesture Sonification to Enhance Awareness of Robot Status and Enjoyment of Interaction

In this study, we aimed to clarify the factors that increase the preference for technology use in nursing care from the perspective of trust. We conducted an online survey on attitudes towards nursing care and technologies, and statistically analyzed the relationships among the preference for technology use in nursing care, trust in technologies, and personal characteristics. The results revealed that those with high trust in intelligent technologies were more likely to prefer the use of such technologies in nursing care.

Analysis on interaction experiences with intelligent technology and preference of technology use in nursing care

This paper presents a haptic shared control paradigm that modulates the level of robotic guidance, based on predictions of human motion intentions. The proposed method incorporates robot trajectories learned from human demonstrations and dynamically adjusts the level of robotic assistance based on how closely the detected intentions match these trajectories. An experimental study is conducted to demonstrate the paradigm on a teleoperated pick-and-place task using a Franka Emika Panda robot arm, controlled via a 3D Systems Touch X haptic interface. In the experiment, the human operator teleoperates a remote robot arm while observing the environment on a 2D screen. While the human teleoperates the robot arm, the objects are tracked, and the human’s motion intentions (e.g., which object will be picked or which bin will be approached) are predicted using a Deep Q-Network (DQN). The predictions are made considering the current robot state and baseline robot trajectories that are learned from human demonstrations using Probabilistic Movement Primitives (ProMPs). The detected intentions are then used to condition the ProMP trajectories to modulate the movement and accommodate changing object configurations. Consequently, the system generates adaptive force guidance as weighted virtual fixtures that are rendered on the haptic device. The outcomes of the user study, conducted with 12 participants, indicate that the proposed paradigm can successfully guide users to robust grasping configurations and brings better performance by reducing the number of grasp attempts and improving trajectory smoothness and length.

Explainable Temperament Estimation of Toddlers by a Childcare Robot

A novel interaction concept and according to inverse kinematic solvers based on a local elliptic approximation are proposed, which allows commanding a WAM holding grasp with one click on a point cloud. It is shown how the local elliptic model is used for formulating a very fast analytic multi-contact inverse kinematic solver. Experiments show the efﬁciency of the analytic method and the robustness of the local elliptic model relative to perceptual perturbations.

Commanding a Whole-Arm Manipulation Grasp Configuration with One Click: Interaction Concept and Analytic IK Method

Currently, there is a lack of study on culture with respect to non-humanoid, multi-robot interactions. In this paper, we explore how cultural differences may impact users’ preferences and perceived usefulness of a multi-robot organizer system by using an interactive online survey. Evaluated across 191 young adults, we find significant differences between participants from India and China or the USA, but not between China and the USA. Our findings reinforce the importance of considering cultural differences in designing domestic multi-robotic assistants.

Towards Infant Kick Quality Detection to Support Physical Therapy and Early Detection of Cerebral Palsy: A Pilot Study

In this work, we show that humanoid robots could form trust in other agents. And the same humanoid robot could scaffold a perceptually limited infant robot by employing learned trust-based strategies.

Trust Me! I Am a Robot: An Affective Computational Account of Scaffolding in Robot-Robot Interaction

This study aimed to evaluate how three different robot interaction styles (i.e., Friendly, Neutral, and Authoritarian) were perceived by humans in the context of a robot carrying out cognitive tests. The Self-Assessment Manikin (SAM) was proposed to measure the perceived Valence, Arousal, and Dominance. We expected that a Neutral behaviour is characterized by low Arousal, a Friendly by high Valence, and an Authoritarian by high Dominance. Moreover, the perception of a Socially Assistive Robot's behaviour is closely linked to trust, hence, a Trust Perception Scale was used to explore the effect of the interaction style on trust.

Towards the Exact Solution for Speed and Separation Monitoring for Improved Human-Robot Collaboration

NATO has issued a challenge seeking to identify ways to improve decision-maker and operator trust when deploying autonomous systems, and de-risk their adoption. This paper presents the proposal of the winning solution to this NATO challenge. It approaches trust as a multi-dimensional concept, by incorporating the four dimensions of human-agent trust establishment in a digital twin context.

Goal Density-based Hindsight Experience Prioritization for Multi-Goal Robot Manipulation Reinforcement Learning

Distances in proxemic human-robot interaction has many determinants. One that has not been studied in detail is the emotion of the user with whom the robot interacts. We conducted a user study to investigate the influence on the user's perception of the robot if the robot shows different proxemic responses after a user expressed a specific emotion.

Students participate and evaluate the design and development of a social robot

In this study, we evaluate the dropping sensation for the development of a wearable lower limb force feedback device that can render both dropping and walking sensations.
The developed device can render the dropping sensation at a smaller height than in reality by decelerating and stopping during the rendering of the drop image. 
The purpose of this study is to clarify the dropping sensation for the difference in human acceleration time and human deceleration acceleration .

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Affective, Cognitive and Behavioural Engagement Detection for Human-Robot Interaction in a Bartending Scenario

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