scholarly journals MECHANICAL DESIGN OF THE HUGGABLE ROBOT PROBO

2011 ◽  
Vol 08 (03) ◽  
pp. 481-511 ◽  
Author(s):  
KRISTOF GORIS ◽  
JELLE SALDIEN ◽  
BRAM VANDERBORGHT ◽  
DIRK LEFEBER
Keyword(s):  
Facial Expressions ◽  
High Precision ◽  
Mechanical Design ◽  
Human Robot Interaction ◽  
Nonverbal Cues ◽  
Special Focus ◽  
Robot Interaction ◽  
Communicative Skills ◽  
Face To Face ◽  
Safety Aspects

This paper reports on the mechanical design of the huggable robot Probo. Its intentions include human–robot interaction (HRI), both physical and cognitive, with a special focus on children. Since most of the communication passes through nonverbal cues and since people rely on face-to-face communication, the focus of Probo's communicative skills lies initially on facial expressions. The robot has 20 high-precision motors in its head and body. They are used to actuate the ears, eyebrows, eyelids, eyes, trunk, mouth, and neck. To build safety aspects intrinsically in the robot's hardware, all the motors are linked with flexible components. In case of a collision, the robot will be elastic and safety will be ensured. The mechanics of Probo are covered by protecting plastic shells, foam, and soft fur. This gives Probo's animal-like look and makes the robot huggable.

scholarly journals Human-Robot Handshaking: A Review

2021 ◽  
Author(s):  
Vignesh Prasad ◽  
Ruth Stock-Homburg ◽  
Jan Peters
Keyword(s):  
Facial Expressions ◽  
Social Contexts ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Natural Interaction ◽  
Internal Factors ◽  
Verbal Behaviour ◽  
Key Factor ◽  
Physical Touch

AbstractFor some years now, the use of social, anthropomorphic robots in various situations has been on the rise. These are robots developed to interact with humans and are equipped with corresponding extremities. They already support human users in various industries, such as retail, gastronomy, hotels, education and healthcare. During such Human-Robot Interaction (HRI) scenarios, physical touch plays a central role in the various applications of social robots as interactive non-verbal behaviour is a key factor in making the interaction more natural. Shaking hands is a simple, natural interaction used commonly in many social contexts and is seen as a symbol of greeting, farewell and congratulations. In this paper, we take a look at the existing state of Human-Robot Handshaking research, categorise the works based on their focus areas, draw out the major findings of these areas while analysing their pitfalls. We mainly see that some form of synchronisation exists during the different phases of the interaction. In addition to this, we also find that additional factors like gaze, voice facial expressions etc. can affect the perception of a robotic handshake and that internal factors like personality and mood can affect the way in which handshaking behaviours are executed by humans. Based on the findings and insights, we finally discuss possible ways forward for research on such physically interactive behaviours.

scholarly journals Predicting Extraversion from Non-verbal Features During a Face-to-Face Human-Robot Interaction

2015 ◽  
pp. 543-553 ◽  
Author(s):  
Faezeh Rahbar ◽  
Salvatore M. Anzalone ◽  
Giovanna Varni ◽  
Elisabetta Zibetti ◽  
Serena Ivaldi ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Face To Face

scholarly journals Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6438
Author(s):  
Chiara Filippini ◽  
David Perpetuini ◽  
Daniela Cardone ◽  
Arcangelo Merla
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Affective Computing ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Expression Recognition ◽  
Robot Interaction ◽  
Interaction Field ◽  
Nao Robot

An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s.

scholarly journals Robot Gaze Behavior Affects Honesty in Human-Robot Interaction

2021 ◽  
Author(s):  
Elef Schellen ◽  
Francesco Bossi ◽  
Agnieszka Wykowska
Keyword(s):  
Social Interaction ◽  
Eye Contact ◽  
Humanoid Robots ◽  
Human Robot Interaction ◽  
Gaze Behavior ◽  
Robot Interaction ◽  
Facial Cues ◽  
Social Agents ◽  
Face To Face ◽  
Deceptive Behavior

As the use of humanoid robots proliferates, an increasing amount of people may find themselves face-to-“face” with a robot in everyday life. Although there is a plethora of information available on facial social cues and how we interpret them in the field of human-human social interaction, we cannot assume that these findings flawlessly transfer to human-robot interaction. Therefore, more research on facial cues in human-robot interaction is required. This study investigated deception in human-robot interaction context, focusing on the effect that eye contact with a robot has on honesty towards this robot. In an iterative task, participants could assist a humanoid robot by providing it with correct information, or potentially secure a reward for themselves by providing it with incorrect information. Results show that participants are increasingly honest after the robot establishes eye contact with them, but only if this is in response to deceptive behavior. Behavior is not influenced by the establishment of eye contact if the participant is actively engaging in honest behavior. These findings support the notion that humanoid robots can be perceived as, and treated like, social agents, since the herein described effect mirrors one present in human-human social interaction.

scholarly journals ExGenNet: Learning to Generate Robotic Facial Expression Using Facial Expression Recognition

2022 ◽  
Vol 8 ◽  
Author(s):  
Niyati Rawal ◽  
Dorothea Koert ◽  
Cigdem Turan ◽  
Kristian Kersting ◽  
Jan Peters ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Human Subjects ◽  
Humanoid Robots ◽  
Human Robot Interaction ◽  
Expression Recognition ◽  
Robot Interaction ◽  
Facial Images ◽  
Intended Expression

The ability of a robot to generate appropriate facial expressions is a key aspect of perceived sociability in human-robot interaction. Yet many existing approaches rely on the use of a set of fixed, preprogrammed joint configurations for expression generation. Automating this process provides potential advantages to scale better to different robot types and various expressions. To this end, we introduce ExGenNet, a novel deep generative approach for facial expressions on humanoid robots. ExGenNets connect a generator network to reconstruct simplified facial images from robot joint configurations with a classifier network for state-of-the-art facial expression recognition. The robots’ joint configurations are optimized for various expressions by backpropagating the loss between the predicted expression and intended expression through the classification network and the generator network. To improve the transfer between human training images and images of different robots, we propose to use extracted features in the classifier as well as in the generator network. Unlike most studies on facial expression generation, ExGenNets can produce multiple configurations for each facial expression and be transferred between robots. Experimental evaluations on two robots with highly human-like faces, Alfie (Furhat Robot) and the android robot Elenoide, show that ExGenNet can successfully generate sets of joint configurations for predefined facial expressions on both robots. This ability of ExGenNet to generate realistic facial expressions was further validated in a pilot study where the majority of human subjects could accurately recognize most of the generated facial expressions on both the robots.

scholarly journals Live human–robot interactive public demonstrations with automatic emotion and personality prediction

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180026 ◽  
Author(s):  
Hatice Gunes ◽  
Oya Celiktutan ◽  
Evangelos Sariyanidi
Keyword(s):  
Facial Expressions ◽  
Humanoid Robot ◽  
Lessons Learned ◽  
Human Robot Interaction ◽  
Theme Issue ◽  
Science And Engineering ◽  
Robot Interaction ◽  
Personality Prediction ◽  
Human Participants ◽  
Public Demonstrations

Communication with humans is a multi-faceted phenomenon where the emotions, personality and non-verbal behaviours, as well as the verbal behaviours, play a significant role, and human–robot interaction (HRI) technologies should respect this complexity to achieve efficient and seamless communication. In this paper, we describe the design and execution of five public demonstrations made with two HRI systems that aimed at automatically sensing and analysing human participants’ non-verbal behaviour and predicting their facial action units, facial expressions and personality in real time while they interacted with a small humanoid robot. We describe an overview of the challenges faced together with the lessons learned from those demonstrations in order to better inform the science and engineering fields to design and build better robots with more purposeful interaction capabilities. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

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