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

Pérez, Carlos Herrera; Barakova, Emilia

doi:10.1007/978-3-030-46732-6_14

Cited by 3 publications

(6 citation statements)

References 44 publications

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“…To the best of our knowledge, this is the first work to address expressive transmission in general terms. Previous works [ 9 , 33 , 41 , 70 , 71 , 72 ] relied on expert descriptors or interactive interfaces to enact expressive and emotive behavior. This is why our experimental setup focuses on analyzing the effectiveness of transmitting human expressivity to the robot.…”

Section: Resultsmentioning

confidence: 99%

“…These qualities can be described numerically following the descriptions proposed in [ 36 , 76 ]. Furthermore, this methodology has been applied to construct, evaluate, and design expressive and legible motions in robots with diverse morphologies [ 25 , 33 , 41 , 70 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

“…These constraints may include legibility, predictability, or any other qualities that might be required according to each robot task. The overarching goal of our approach is to generate an expressive robot movement that can understand and integrate the expressive qualities of human movement inputs, since any embodied behavior can help transmit these essential cues [ 33 ].…”

Section: Contributionmentioning

confidence: 99%

“…Our method can understand the expressive qualities of human movement and exploit them to generate a new movement for the robot. Unlike previous approaches where direct manipulation of the robot’s trajectory, control, and motion qualities, e.g., acceleration, velocity, and position, are the essence of the expressive definition [ 33 , 41 , 70 , 71 , 72 ], our method extracts the underlying qualities from the human, and then integrates them into the robot task, generating a new movement for the robot. This allows for a direct interaction between user and robot, and removes the need for expert design morphology-dependent constraints and the constant reprogramming seen in previous methods.…”

Section: Contributionmentioning

confidence: 99%

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A Generative Model to Embed Human Expressivity into Robot Motions

Osorio,

Sagawa,

Abe

et al. 2024

Sensors

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This paper presents a model for generating expressive robot motions based on human expressive movements. The proposed data-driven approach combines variational autoencoders and a generative adversarial network framework to extract the essential features of human expressive motion and generate expressive robot motion accordingly. The primary objective was to transfer the underlying expressive features from human to robot motion. The input to the model consists of the robot task defined by the robot’s linear velocities and angular velocities and the expressive data defined by the movement of a human body part, represented by the acceleration and angular velocity. The experimental results show that the model can effectively recognize and transfer expressive cues to the robot, producing new movements that incorporate the expressive qualities derived from the human input. Furthermore, the generated motions exhibited variability with different human inputs, highlighting the ability of the model to produce diverse outputs.

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Section: Resultsmentioning

confidence: 99%

Section: Materials and Methodsmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

Section: Contributionmentioning

confidence: 99%

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A Generative Model to Embed Human Expressivity into Robot Motions

Osorio,

Sagawa,

Abe

et al. 2024

Sensors

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“…In order to nevertheless investigate how such important processes take place in a human-robot team, the approach of observing and analyzing embodied human-robot team behavior was taken. Expressivity and intentionality of behavior plays a large role in embodied interaction ( Herrera and Barakova, 2020 ). It is believed that in such a setting the subtle and perhaps unconsciously executed adaptations will be expressed by means of physical, embodied interactions, hence being accessible for observation and analysis.…”

Section: Research Challengementioning

confidence: 99%

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors

et al. 2021

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As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.

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