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Lowe, Robert
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Publications (10 of 62) Show all publications
Andreasson, R., Alenljung, B., Billing, E. & Lowe, R. (2018). Affective Touch in Human–Robot Interaction: Conveying Emotion to the Nao Robot. International Journal of Social Robotics, 10(4), 473-491
Open this publication in new window or tab >>Affective Touch in Human–Robot Interaction: Conveying Emotion to the Nao Robot
2018 (English)In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 10, no 4, p. 473-491Article in journal (Refereed) Published
Abstract [en]

Affective touch has a fundamental role in human development, social bonding, and for providing emotional support in interpersonal relationships. We present, what is to our knowledge, the first HRI study of tactile conveyance of both positive and negative emotions (affective touch) on the Nao robot, and based on an experimental set-up from a study of human–human tactile communication. In the present work, participants conveyed eight emotions to a small humanoid robot via touch. We found that female participants conveyed emotions for a longer time, using more varied interaction and touching more regions on the robot’s body, compared to male participants. Several differences between emotions were found such that emotions could be classified by the valence of the emotion conveyed, by combining touch amount and duration. Overall, these results show high agreement with those reported for human–human affective tactile communication and could also have impact on the design and placement of tactile sensors on humanoid robots.

Place, publisher, year, edition, pages
Springer, 2018
Keywords
Tactile interaction, Affective touch, Human–robot interaction, Emotion encoding, Emotion decoding, Social emotions, Nao robot
National Category
Human Computer Interaction
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-14563 (URN)10.1007/s12369-017-0446-3 (DOI)2-s2.0-85053554592 (Scopus ID)
Projects
Design, textil och hållbar utveckling
Funder
Region Västra Götaland
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-09-28Bibliographically approved
Lowe, R., Andreasson, R., Alenljung, B., Lund, A. & Billing, E. (2018). Designing for a Wearable Affective Interface for the NAO Robot: A Study of Emotion Conveyance by Touch. Multimodal Technologies and Interaction, 2(1)
Open this publication in new window or tab >>Designing for a Wearable Affective Interface for the NAO Robot: A Study of Emotion Conveyance by Touch
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2018 (English)In: Multimodal Technologies and Interaction, ISSN 2414-4088, Vol. 2, no 1Article in journal (Refereed) Published
Abstract [en]

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.

Keywords
affective tactile interaction, emotions, human-robot interaction, touch, emotion classification
National Category
Robotics
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-14958 (URN)10.3390/mti2010002 (DOI)
Projects
Design, textil och hållbar utveckling
Funder
Region Västra Götaland
Available from: 2018-03-13 Created: 2018-03-13 Last updated: 2018-04-25Bibliographically approved
Lowe, R. & Billing, E. (2017). Affective-Associative Two-Process theory: A neural network investigation of adaptive behaviour in differential outcomes training. Adaptive Behavior, 25(1), 5-23
Open this publication in new window or tab >>Affective-Associative Two-Process theory: A neural network investigation of adaptive behaviour in differential outcomes training
2017 (English)In: Adaptive Behavior, ISSN 1059-7123, E-ISSN 1741-2633, Vol. 25, no 1, p. 5-23Article in journal (Refereed) Published
Abstract [en]

In this article we present a novel neural network implementation of Associative Two-Process (ATP) theory based on an Actor–Critic-like architecture. Our implementation emphasizes the affective components of differential reward magnitude and reward omission expectation and thus we model Affective-Associative Two-Process theory (Aff-ATP). ATP has been used to explain the findings of differential outcomes training (DOT) procedures, which emphasize learning differentially valuated outcomes for cueing actions previously associated with those outcomes. ATP hypothesizes the existence of a ‘prospective’ memory route through which outcome expectations can bring to bear on decision making and can even substitute for decision making based on the ‘retrospective’ inputs of standard working memory. While DOT procedures are well recognized in the animal learning literature they have not previously been computationally modelled. The model presented in this article helps clarify the role of ATP computationally through the capturing of empirical data based on DOT. Our Aff-ATP model illuminates the different roles that prospective and retrospective memory can have in decision making (combining inputs to action selection functions). In specific cases, the model’s prospective route allows for adaptive switching (correct action selection prior to learning) following changes in the stimulus–response–outcome contingencies.

Place, publisher, year, edition, pages
Sage Publications, 2017
Keywords
Actor-Critic, Reward learning, Animal models, Associative Two-Process theory, Neo-behaviourism, Decision making
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Natural sciences; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-13405 (URN)10.1177/1059712316682999 (DOI)000394678600002 ()2-s2.0-85012120144 (Scopus ID)
Funder
EU, FP7, Seventh Framework Programme, 270247
Available from: 2017-02-23 Created: 2017-02-23 Last updated: 2018-09-12Bibliographically approved
Lowe, R., Almér, A., Billing, E., Sandamirskaya, Y. & Balkenius, C. (2017). Affective–associative two-process theory: a neurocomputational account of partial reinforcement extinction effects. Biological Cybernetics, 111(5-6), 365-388
Open this publication in new window or tab >>Affective–associative two-process theory: a neurocomputational account of partial reinforcement extinction effects
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2017 (English)In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 111, no 5-6, p. 365-388Article in journal (Refereed) Published
Abstract [en]

The partial reinforcement extinction effect (PREE) is an experimentally established phenomenon: behavioural response to a given stimulus is more persistent when previously inconsistently rewarded than when consistently rewarded. This phenomenon is, however, controversial in animal/human learning theory. Contradictory findings exist regarding when the PREE occurs. One body of research has found a within-subjects PREE, while another has found a within-subjects reversed PREE (RPREE). These opposing findings constitute what is considered the most important problem of PREE for theoreticians to explain. Here, we provide a neurocomputational account of the PREE, which helps to reconcile these seemingly contradictory findings of within-subjects experimental conditions. The performance of our model demonstrates how omission expectancy, learned according to low probability reward, comes to control response choice following discontinuation of reward presentation (extinction). We find that a PREE will occur when multiple responses become controlled by omission expectation in extinction, but not when only one omission-mediated response is available. Our model exploits the affective states of reward acquisition and reward omission expectancy in order to differentially classify stimuli and differentially mediate response choice. We demonstrate that stimulus–response (retrospective) and stimulus–expectation–response (prospective) routes are required to provide a necessary and sufficient explanation of the PREE versus RPREE data and that Omission representation is key for explaining the nonlinear nature of extinction data.

Place, publisher, year, edition, pages
Springer, 2017
Keywords
Partial reinforcement, Reinforcement learning, Decision making, Associative two-process theory, Affect
National Category
Other Computer and Information Science
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-14392 (URN)10.1007/s00422-017-0730-1 (DOI)000415625500004 ()28913644 (PubMedID)2-s2.0-85029510456 (Scopus ID)
Projects
NeuralDynamics, 7th framework of the EU, grant #270247
Funder
EU, FP7, Seventh Framework Programme, 270247
Available from: 2017-11-11 Created: 2017-11-11 Last updated: 2018-02-01Bibliographically approved
Navarro-Guerrero, N., Lowe, R. J. & Wermter, S. (2017). Improving Robot Motor Learning with Negatively Valenced Reinforcement Signals. Frontiers in Neurorobotics, 11, Article ID 10.
Open this publication in new window or tab >>Improving Robot Motor Learning with Negatively Valenced Reinforcement Signals
2017 (English)In: Frontiers in Neurorobotics, ISSN 1662-5218, Vol. 11, article id 10Article in journal (Refereed) Published
Abstract [en]

Both nociception and punishment signals have been used in robotics. However, the potential for using these negatively valenced types of reinforcement learning signals for robot learning has not been exploited in detail yet. Nociceptive signals are primarily used as triggers of preprogrammed action sequences. Punishment signals are typically disembodied, i. e., with no or little relation to the agent-intrinsic limitations, and they are often used to impose behavioral constraints. Here, we provide an alternative approach for nociceptive signals as drivers of learning rather than simple triggers of preprogrammed behavior. Explicitly, we use nociception to expand the state space while we use punishment as a negative reinforcement learning signal. We compare the performance-in terms of task error, the amount of perceived nociception, and length of learned action sequences-of different neural networks imbued with punishment-based reinforcement signals for inverse kinematic learning. We contrast the performance of a version of the neural network that receives nociceptive inputs to that without such a process. Furthermore, we provide evidence that nociception can improve learning-making the algorithm more robust against network initializations-as well as behavioral performance by reducing the task error, perceived nociception, and length of learned action sequences. Moreover, we provide evidence that punishment, at least as typically used within reinforcement learning applications, may be detrimental in all relevant metrics.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2017
Keywords
reinforcement learning, inverse kinematics, nociception, punishment, self-protective mechanisms
National Category
Computer and Information Sciences
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-13548 (URN)10.3389/fnbot.2017.00010 (DOI)000399141900001 ()28420976 (PubMedID)2-s2.0-85018457189 (Scopus ID)
Available from: 2017-05-05 Created: 2017-05-05 Last updated: 2018-01-13Bibliographically approved
Lowe, R., Dodig-Crnkovic, G. & Almer, A. (2017). Predictive regulation in affective and adaptive behaviour: An allostatic-cybernetics perspective. In: Jordi Vallverdú, Manuel Mazzara, Max Talanov, Salvatore Distefano and Robert Lowe (Ed.), Advanced Research on Biologically Inspired Cognitive Architectures: (pp. 149-176). IGI Global
Open this publication in new window or tab >>Predictive regulation in affective and adaptive behaviour: An allostatic-cybernetics perspective
2017 (English)In: Advanced Research on Biologically Inspired Cognitive Architectures / [ed] Jordi Vallverdú, Manuel Mazzara, Max Talanov, Salvatore Distefano and Robert Lowe, IGI Global, 2017, p. 149-176Chapter in book (Other academic)
Abstract [en]

In this chapter, different notions of allostasis (the process of achieving stability through change ) as they apply to adaptive behavior are presented. The authors discuss how notions of allostasis can be usefully applied to Cybernetics-based homeostatic systems. Particular emphasis is placed upon affective states - motivational and emotional - and, above all, the notion of 'predictive' regulation, as distinct from forms of 'reactive' regulation, in homeostatic systems. The authors focus here on Ashby's ultrastability concept that entails behavior change for correcting homeostatic errors (deviations from the healthy range of essential, physiological, variables). The authors consider how the ultrastability concept can be broadened to incorporate allostatic mechanisms and how they may enhance adaptive physiological and behavioral activity. Finally, this chapter references different Cybernetics frameworks that incorporate the notion of allostasis. The article then attempts to untangle how the given perspectives fit into the 'allostatic ultrastable systems' framework postulated. 

Place, publisher, year, edition, pages
IGI Global, 2017
Series
Advances in Computational Intelligence and Robotics (ACIR), ISSN 2327-0411, E-ISSN 2327-042X
National Category
Computer and Information Sciences
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-14569 (URN)10.4018/978-1-5225-1947-8.ch008 (DOI)000411555200009 ()2-s2.0-85027510045 (Scopus ID)978-1-5225-1948-5 (ISBN)978-1-5225-1947-8 (ISBN)1-5225-1947-5 (ISBN)
Available from: 2017-12-07 Created: 2017-12-07 Last updated: 2018-02-01Bibliographically approved
Alenljung, B., Andreasson, R., Billing, E. A., Lindblom, J. & Lowe, R. (2017). User Experience of Conveying Emotions by Touch. In: Proceedings of the 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN): . Paper presented at IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) Lisbon, Portugal, Aug 28 - Sept 1, 2017 (pp. 1240-1247). IEEE
Open this publication in new window or tab >>User Experience of Conveying Emotions by Touch
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2017 (English)In: Proceedings of the 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), IEEE, 2017, p. 1240-1247Conference paper, Published paper (Refereed)
Abstract [en]

In the present study, 64 users were asked to convey eight distinct emotion to a humanoid Nao robot via touch, and were then asked to evaluate their experiences of performing that task. Large differences between emotions were revealed. Users perceived conveying of positive/pro-social emotions as significantly easier than negative emotions, with love and disgust as the two extremes. When asked whether they would act differently towards a human, compared to the robot, the users’ replies varied. A content analysis of interviews revealed a generally positive user experience (UX) while interacting with the robot, but users also found the task challenging in several ways. Three major themes with impact on the UX emerged; responsiveness, robustness, and trickiness. The results are discussed in relation to a study of human-human affective tactile interaction, with implications for human-robot interaction (HRI) and design of social and affective robotics in particular. 

Place, publisher, year, edition, pages
IEEE, 2017
Series
International Symposium on Robot and Human Interactive Communication, ISSN 1944-9445, E-ISSN 1944-9437
National Category
Robotics
Research subject
Interaction Lab (ILAB); INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-14082 (URN)10.1109/ROMAN.2017.8172463 (DOI)000427262400193 ()2-s2.0-85034038084 (Scopus ID)978-1-5386-3517-9 (ISBN)978-1-5386-3519-3 (ISBN)978-1-5386-3518-6 (ISBN)
Conference
IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) Lisbon, Portugal, Aug 28 - Sept 1, 2017
Projects
Design, Textil och hållbar Utveckling (VGR)
Funder
Region Västra Götaland
Available from: 2017-09-04 Created: 2017-09-04 Last updated: 2018-08-31
Montebelli, A. & Lowe, R. (2016). (Em)powering Emergent Cognition: Realistic proto-allostasis as a foundational route to cognitive ability. In: : . Paper presented at EUCognition Meeting 08-09.12.2016 in Vienna.
Open this publication in new window or tab >>(Em)powering Emergent Cognition: Realistic proto-allostasis as a foundational route to cognitive ability
2016 (English)Conference paper, Oral presentation with published abstract (Refereed)
Keywords
robot autonomy, allostasis, energy management
National Category
Other Computer and Information Science
Research subject
Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-13344 (URN)
Conference
EUCognition Meeting 08-09.12.2016 in Vienna
Available from: 2017-01-30 Created: 2017-01-30 Last updated: 2018-08-03Bibliographically approved
Billing, E., Svensson, H., Lowe, R. & Ziemke, T. (2016). Finding Your Way from the Bed to the Kitchen: Re-enacting and Re-combining Sensorimotor Episodes Learned from Human Demonstration. Frontiers in Robotics and AI, 3(9)
Open this publication in new window or tab >>Finding Your Way from the Bed to the Kitchen: Re-enacting and Re-combining Sensorimotor Episodes Learned from Human Demonstration
2016 (English)In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 3, no 9Article in journal (Refereed) Published
Abstract [en]

Several simulation theories have been proposed as an explanation for how humans and other agents internalize an "inner world" that allows them to simulate interactions with the external real world - prospectively and retrospectively. Such internal simulation of interaction with the environment has been argued to be a key mechanism behind mentalizing and planning. In the present work, we study internal simulations in a robot acting in a simulated human environment. A model of sensory-motor interactions with the environment is generated from human demonstrations, and tested on a Robosoft Kompai robot. The model is used as a controller for the robot, reproducing the demonstrated behavior. Information from several different demonstrations is mixed, allowing the robot to produce novel paths through the environment, towards a goal specified by top-down contextual information. 

The robot model is also used in a covert mode, where actions are inhibited and perceptions are generated by a forward model. As a result, the robot generates an internal simulation of the sensory-motor interactions with the environment. Similar to the overt mode, the model is able to reproduce the demonstrated behavior as internal simulations. When experiences from several demonstrations are combined with a top-down goal signal, the system produces internal simulations of novel paths through the environment. These results can be understood as the robot imagining an "inner world" generated from previous experience, allowing it to try out different possible futures without executing actions overtly.

We found that the success rate in terms of reaching the specified goal was higher during internal simulation, compared to overt action. These results are linked to a reduction in prediction errors generated during covert action. Despite the fact that the model is quite successful in terms of generating covert behavior towards specified goals, internal simulations display different temporal distributions compared to their overt counterparts. Links to human cognition and specifically mental imagery are discussed.

Place, publisher, year, edition, pages
Lausanne, Switzerland: Frontiers, 2016
Keywords
compositionality, internal simulation, learning from demonstration, simulation theory, predictive sequence learning, prospection, embodied cognition, imagination, representation
National Category
Computer and Information Sciences
Research subject
Technology; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-12075 (URN)10.3389/frobt.2016.00009 (DOI)000382475100002 ()
Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2018-08-14Bibliographically approved
Lowe, R., Barakova, E., Billing, E. & Broekens, J. (2016). Grounding emotions in robots: An introduction to the special issue. Adaptive Behavior, 24(5), 263-266
Open this publication in new window or tab >>Grounding emotions in robots: An introduction to the special issue
2016 (English)In: Adaptive Behavior, ISSN 1059-7123, E-ISSN 1741-2633, Vol. 24, no 5, p. 263-266Article in journal, Editorial material (Refereed) Published
Abstract [en]

Robots inhabiting human environments need to act in relation to their own experience and embodiment as well as to social and emotional aspects. Robots that learn, act upon and incorporate their own experience and perception of others’ emotions into their responses make not only more productive artificial agents but also agents with whom humans can appropriately interact. This special issue seeks to address the significance of grounding of emotions in robots in relation to aspects of physical and homeostatic interaction in the world at an individual and social level. Specific questions concern: How can emotion and social interaction be grounded in the behavioral activity of the robotic system? Is a robot able to have intrinsic emotions? How can emotions, grounded in the embodiment of the robot, facilitate individually and socially adaptive behavior to the robot? This opening chapter provides an introduction to the articles that comprise this special issue and briefly discusses their relationship to grounding emotions in robots.

Place, publisher, year, edition, pages
Sage Publications, 2016
Keywords
emotions, grounding, social interaction, intrinsic processes
National Category
Human Computer Interaction
Research subject
Natural sciences; Interaction Lab (ILAB)
Identifiers
urn:nbn:se:his:diva-13084 (URN)10.1177/1059712316668239 (DOI)000386958600001 ()2-s2.0-84994106761 (Scopus ID)
Available from: 2016-11-09 Created: 2016-11-09 Last updated: 2018-08-03Bibliographically approved
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