Combining intention and emotional state inference in a dynamic neural field architecture for human-robot joint action

Silva, Rui; Louro, Luís; Malheiro, Tiago; Erlhagen, Wolfram; Bicho, Estela

doi:10.1177/1059712316665451

Cited by 5 publications

(5 citation statements)

References 75 publications

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“…Interestingly, these neuroscientific findings are in line with behavioral studies showing that humans seem to be willing to treat robots as entities with agency (i.e., ability to plan and act), but are reluctant to perceive them as entities that can experience internal states (i.e., ability to sense and feel; Gray et al, 2007 ). In consequence, research in social robotics would benefit from identifying conditions under which artificial agents engage mechanisms of higher-order social cognition in the human brain, which may necessitate some effort to specifically design robots as intentional and empathetic agents ( Gonsior et al, 2012 ; Silva et al, 2016 ).…”

Section: Observing Intentional Agents Activates Social Brain Areasmentioning

confidence: 99%

Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

2017

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Robots are increasingly envisaged as our future cohabitants. However, while considerable progress has been made in recent years in terms of their technological realization, the ability of robots to interact with humans in an intuitive and social way is still quite limited. An important challenge for social robotics is to determine how to design robots that can perceive the user’s needs, feelings, and intentions, and adapt to users over a broad range of cognitive abilities. It is conceivable that if robots were able to adequately demonstrate these skills, humans would eventually accept them as social companions. We argue that the best way to achieve this is using a systematic experimental approach based on behavioral and physiological neuroscience methods such as motion/eye-tracking, electroencephalography, or functional near-infrared spectroscopy embedded in interactive human–robot paradigms. This approach requires understanding how humans interact with each other, how they perform tasks together and how they develop feelings of social connection over time, and using these insights to formulate design principles that make social robots attuned to the workings of the human brain. In this review, we put forward the argument that the likelihood of artificial agents being perceived as social companions can be increased by designing them in a way that they are perceived as intentional agents that activate areas in the human brain involved in social-cognitive processing. We first review literature related to social-cognitive processes and mechanisms involved in human–human interactions, and highlight the importance of perceiving others as intentional agents to activate these social brain areas. We then discuss how attribution of intentionality can positively affect human–robot interaction by (a) fostering feelings of social connection, empathy and prosociality, and by (b) enhancing performance on joint human–robot tasks. Lastly, we describe circumstances under which attribution of intentionality to robot agents might be disadvantageous, and discuss challenges associated with designing social robots that are inspired by neuroscientific principles.

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Section: Observing Intentional Agents Activates Social Brain Areasmentioning

confidence: 99%

Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

2017

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“…Nevertheless, where tactile interaction might be particularly relevant in human-robot interaction is in the domain of Joint Action (cf. [47][48][49]) where interaction on a goal-directed task may benefit from human actors conveying both negative (rejection-based) and positive (e.g., gratitude) feedback. Again, in reference to [45], communicating emotions such as anger/frustration may be critically important to informing interactors as to how the task is perceived to be going and how to respond accordingly (e.g., approach the task in a different way, or try harder).…”

mentioning

confidence: 99%

Designing for a Wearable Affective Interface for the NAO Robot: A Study of Emotion Conveyance by Touch

Lowe

Andreasson

Alenljung

et al. 2018

MTI

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Abstract:We here present results and analysis from a study of affective tactile communication between human and humanoid robot (the NAO robot). In the present work, participants conveyed eight emotions to the NAO via touch. In this study, we sought to understand the potential for using a wearable affective (tactile) interface, or WAffI. The aims of our study were to address the following: (i) how emotions and affective states can be conveyed (encoded) to such a humanoid robot, (ii) what are the effects of dressing the NAO in the WAffI on emotion conveyance and (iii) what is the potential for decoding emotion and affective states. We found that subjects conveyed touch for longer duration and over more locations on the robot when the NAO was dressed with WAffI than when it was not. Our analysis illuminates ways by which affective valence, and separate emotions, might be decoded by a humanoid robot according to the different features of touch: intensity, duration, location, type. Finally, we discuss the types of sensors and their distribution as they may be embedded within the WAffI and that would likely benefit Human-NAO (and Human-Humanoid) interaction along the affective tactile dimension.

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“…It implements a highly context-sensitive mapping of an observed action of the co-worker onto an adequate complementary robot behavior. This mapping takes into account different task-related and user-related factors including an error monitoring capacity [6,8,54]. Neural populations encode in their suprathreshold activity a mismatch between which assembly step the operator should execute (shared task knowledge) and the predicted assembly step inferred from the observed motor behavior.…”

Section: Discussionmentioning

confidence: 99%

A neural integrator model for planning and value-based decision making of a robotics assistant

Wojtak

Ferreira

Vicente

et al. 2020

Neural Comput & Applic

Self Cite

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Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human-robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that applies brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over

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Combining intention and emotional state inference in a dynamic neural field architecture for human-robot joint action

Cited by 5 publications

References 75 publications

Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

Robots As Intentional Agents: Using Neuroscientific Methods to Make Robots Appear More Social

Designing for a Wearable Affective Interface for the NAO Robot: A Study of Emotion Conveyance by Touch

A neural integrator model for planning and value-based decision making of a robotics assistant

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