Expressing Emotions with the Social Robot Probo

Saldien, Jelle; Goris, Kristof; Vanderborght, Bram; Vanderfaeillie, Johan; Lefeber, Dirk

doi:10.1007/s12369-010-0067-6

Cited by 138 publications

(106 citation statements)

References 22 publications

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“…showed that people are able to recognize the emotional expressions of Kismet (Breazeal, 2002) and Probo (Saldien et al 2010). Such evaluations are useful, and show that it is possible to develop robots that can form recognizable emotional expressions.…”

Section: Emotion and Contextmentioning

confidence: 93%

“…For instance, the FACS was widely adapted to create at least six basic and discrete facial expressions (joy, sadness, anger, fear, disgust and surprise) for some non humanoid robotssuch as Feelix (Canamero and Fredslund, 2000), Kismet (Breazeal, 2002), Aryan (Mobahi et al 2003), EDDIE (Sosnowski et al 2006), and Probo (Saldien et al 2010). The FACS was initially developed as a set of guidelines for recognizing the facial expressions of humans but has been found to be a reliable tool for creating believable versions of these basic facial expressions (e.g., overall recognition rate of the basic facial expressions for Probo was 84%, followed by Kismet with 73%, while Eddie achieved a bit lower rate with 57%.…”

Section: Creating Recognizable Facial Expressions For Social Robotsmentioning

confidence: 99%

“…The FACS was initially developed as a set of guidelines for recognizing the facial expressions of humans but has been found to be a reliable tool for creating believable versions of these basic facial expressions (e.g., overall recognition rate of the basic facial expressions for Probo was 84%, followed by Kismet with 73%, while Eddie achieved a bit lower rate with 57%. ), even though some of these evaluations were based on posed but not spontaneous robot facial expressions and some facial expressions (fear, and happy+surprised) were harder to make convincing (Breazeal, 2002;Saldien et al 2010). …”

Section: Creating Recognizable Facial Expressions For Social Robotsmentioning

confidence: 99%

“…The appearance of the robot represents an imaginary animal based on the ancient mammoths, which has less resemblance to existing animals or humans aiming to avoid Mori's problem of the -uncanny valley‖ (Mori, 1981), according to (Saldien et al 2010). …”

Section: B Probomentioning

confidence: 99%

“…Some of the tests are heavily impacted by the Kismet project. Details of all the 7 tests are elaboratively described in (Saldien et al 2010). In brief, during the design phase, a virtual model of Probo was built to simulate the facial expressions used in the first five recognition tests (T1-T5).…”

Section: B Probomentioning

confidence: 99%

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Contextual Recognition of Robot Emotions

Zhang

Sharkey

2011

Lecture Notes in Computer Science

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In the field of human-robot interaction, socially interactive robots are often equipped with the ability to detect the affective states of users, the ability to express emotions through the use of synthetic facial expressions, speech and textual content, and the ability for imitating and social learning. Past work on creating robots that can make convincing emotional expressions has concentrated on the quality of those expressions, and on assessing people's ability to recognize them. Previous recognition studies presented the facial expressions of the robots in neutral contexts, without any strong emotional valence (e.g., emotionally valenced music or video). It is therefore worth empirically exploring whether observers' judgments of the facial cues of a robot would be affected by a surrounding emotional context. This thesis takes its inspiration from the contextual effects found on the interpretation of the expressions on human faces and computer avatars, and looks at the extent to which they also apply to the interpretation of the facial expressions of a mechanical robot head. The kinds of contexts that affect the recognition of robot emotional expressions, the circumstances under which such contextual effects occur, and the relationship between emotions and the surrounding situation, are observed and analyzed in a series of 11 experiments. In these experiments, the FACS (Facial Action Coding System) (Ekman and Friesen, 2002) was applied to set up the parameters of the servos to make the robot head produce sequences of facial expressions. Four different emotional surrounding or preceding contexts were used (i.e., recorded BBC News pieces, selected affective pictures, classical music pieces and film clips). This thesis provides evidence that observers' judgments about the facial expressions of a robot can be affected by a surrounding emotional context. From a psychological perspective, the contextual effects found on the robotic facial expressions based on the FACS, indirectly support the claims that human emotions are both 4 biologically based and socially constructed. From a robotics perspective, it is argued that the results obtained from the analyses will be useful for guiding researchers to enhance the expressive skills of emotional robots in a surrounding emotional context. This thesis also analyzes the possible factors contributing to the contextual effects found in the original 11 experiments.Some future work, including four new experiments (a preliminary experiment designed to identify appropriate contextual materials and three further experiments in which factors likely to affect a context effect are controlled one by one) is also proposed in this thesis.

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Section: Emotion and Contextmentioning

confidence: 93%

Section: Creating Recognizable Facial Expressions For Social Robotsmentioning

confidence: 99%

Section: Creating Recognizable Facial Expressions For Social Robotsmentioning

confidence: 99%

Section: B Probomentioning

confidence: 99%

Section: B Probomentioning

confidence: 99%

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Contextual Recognition of Robot Emotions

Zhang

Sharkey

2011

Lecture Notes in Computer Science

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Processing of Self‐Healing Polymers for Soft Robotics

et al. 2021

Self Cite

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Soft robots are, due to their softness, inherently safe and adapt well to unstructured environments. However, they are prone to various damage types. Self‐healing polymers address this vulnerability. Self‐healing soft robots can recover completely from macroscopic damage, extending their lifetime. For developing healable soft robots, various formative and additive manufacturing methods have been exploited to shape self‐healing polymers into complex structures. Additionally, several novel manufacturing techniques, noted as (re)assembly binding techniques that are specific to self‐healing polymers, have been created. Herein, the wide variety of processing techniques of self‐healing polymers for robotics available in the literature is reviewed, and limitations and opportunities discussed thoroughly. Based on defined requirements for soft robots, these techniques are critically compared and validated. A strong focus is drawn to the reversible covalent and (physico)chemical cross‐links present in the self‐healing polymers that do not only endow healability to the resulting soft robotic components, but are also beneficial in many manufacturing techniques. They solve current obstacles in soft robots, including the formation of robust multi‐material parts, recyclability, and stress relaxation. This review bridges two promising research fields, and guides the reader toward selecting a suitable processing method based on a self‐healing polymer and the intended soft robotics application.

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Expressivity Comes First, Movement Follows: Embodied Interaction as Intrinsically Expressive Driver of Robot Behaviour

Pérez

Barakova

2020

Modelling Human Motion

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Expressing Emotions with the Social Robot Probo

Cited by 138 publications

References 22 publications

Contextual Recognition of Robot Emotions

Contextual Recognition of Robot Emotions

Processing of Self‐Healing Polymers for Soft Robotics

Expressivity Comes First, Movement Follows: Embodied Interaction as Intrinsically Expressive Driver of Robot Behaviour

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