Emotion-based control of cooperating heterogeneous mobile robots

Murphy, Robin R.; Lisetti, Christine L.; Tardif, R.; Irish, L.; Gage, A.

doi:10.1109/tra.2002.804503

Cited by 78 publications

(51 citation statements)

References 29 publications

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“…Finally, recent work in multi-agent teamwork suggests that in virtual organizations, agents that simulate emotions would be more believable to humans and could anticipate human needs by appropriate modeling of others [15,20].…”

Section: Cognitive Architecturesmentioning

confidence: 99%

Emotions: from brain to robot

Arbib

Fellous

2004

Trends in Cognitive Sciences

214

114

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Some robots have been given emotional expressions in an attempt to improve human-computer interaction. In this article we analyze what it would mean for a robot to have emotion, distinguishing emotional expression for communication from emotion as a mechanism for the organization of behavior. Research on the neurobiology of emotion yields a deepening understanding of interacting brain structures and neural mechanisms rooted in neuromodulation that underlie emotions in humans and other animals. However, the chemical basis of animal function differs greatly from the mechanics and computations of current machines. We therefore abstract from biology a functional characterization of emotion that does not depend on physical substrate or evolutionary history, and is broad enough to encompass the possible emotions of robots.Interest in the creation of robots with emotions is fourfold. First, current technology already shows the value of providing robots with 'emotional' expressions (e.g. computer tutors) and bodily postures (e.g. robot pets) to facilitate human-computer interaction. Second, this raises the question of the possible value (or inevitability) of future robots not only simulating emotional expression but actually 'having emotions'. Third, this in turn requires us to re-examine the neurobiology of emotion to generalize concepts first developed for humans and then extended to animals so that the question of robot emotions becomes meaningful. And fourth, this suggests in turn that building 'emotional robots' could also provide a novel test-bed for theories of biological emotion.This article samples the state of the art on current robot technology, and examines recent work on the neurobiology of emotions, to ground our suggestions for a scientific framework in which to approach robot 'emotions'. The question of 'emotional robots' being used to test theories of biological emotion is of great interest, but beyond the scope of this article. Different kinds of emotionsThere is a wide spectrum of feelings, from the 'motivation' afforded by drives such as the search for food afforded by hunger [1] to 'emotions' in which, at least in humans, cognitive awareness might be linked to feeling the 'heat' of love, sorrow or anger, and so on. But as we have no criterion for saying that a robot has 'feelings', we will seek here to understand emotions in their functional context, noting that not all emotions need be like human emotions. We analyze emotion in two main senses:(1) Emotional expression for communication and social coordination.(2) Emotion for organization of behavior (action selection, attention and learning).The first concerns 'external' aspect of emotions; the second 'internal' aspects. In animals, these aspects have co-evolved. How might they enter robot design? Both robots and animals need to survive and perform efficiently within their 'ecological niche' and, in each case, patterns of coordination will greatly influence the suite of relevant emotions (if such are indeed needed) and the means whereby they are co...

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Section: Cognitive Architecturesmentioning

confidence: 99%

Emotions: from brain to robot

Arbib

Fellous

2004

Trends in Cognitive Sciences

214

114

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“…In the last years, the realization of the extreme importance that the non-cognitive factors of behavior play in organisms' behavior (Parisi, 2004;Arbib and Fellous, 2004;Canamero, 2005) has significantly boosted the number of researches dedicated to these aspects in the fields of artificial life and autonomous robotics (e.g. Canamero, 1997;Balkenius and Morén, 1999;Murphy, 2002;Mirolli and Parisi, 2003;AvilaGarcia and Canamero, 2004;Montebelli et al, 2008;Venditti et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

The roles of the amygdala in the affective regulation of body, brain, and behaviour

2010

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Abstract. Despite the great amount of knowledge produced by the neuroscientific literature affective phenomena, current models tackling noncognitive aspects of behavior are often bio-inspired but rarely bio-constrained. This paper presents a theoretical account of affective systems centered on the amygdala. This account aims to furnish a general framework and specific pathways to implement models that are more closely related to biological evidence. The amygdala, which receives input from brain areas encoding internal states, innately relevant stimuli, and innately neutral stimuli, plays a fundamental role in motivational and emotional processes of organisms. This role is based on the fact that amygdala implements the two associative processes at the core of Pavlovian learning (CS-US and CS-UR associations), and that it has the capacity of modulating these associations on the basis of internal states. These functionalities allow the amygdala to play an important role in the regulation of the three fundamental classes of affective responses (namely, the regulation of body states, the regulation of brain states via neuromodulators, and the triggering of a number of basic behaviours fundamental for adaptation) and in the regulation of three high-level cognitive processes (namely, the affective labeling of memories, the production of goal-directed behaviours, and the performance of planning and complex decision making). Our analysis is conducted within a methodological approach that stresses the importance of understanding the brain within an evolutionary/adaptive framework and with the aim of isolating general principles that can potentially account for the wider possible empirical evidence in a coherent fashion.

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“…It is doubtful that we would attribute consciousness to all the integrative societies in the animal kingdom; thus, some mechanism must exist for achieving this type of productive interaction without the need for higherlevel cognition. A large body of multi-robot systems research falls into this category (e.g., [82], [123], [78], [12], [15], [67], [101], [103], [119], [1]). The next two paradigms discussed in this article -namely, the organizational/social paradigm and the knowledge-based/ontological/semantic paradigmare additional techniques that have been used to create similar higher-level, intentional cooperation and/or collaboration in multi-robot teams.…”

Section: A Bioinspired Emergent Swarms Paradigmmentioning

confidence: 99%

Distributed intelligence: overview of the field and its application in multi-robot systems

Parker¹

2008

jopha

151

107

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Abstract-This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the solution paradigm to be used. We outline three common paradigms for distributed intelligence -the bioinspired paradigm, the organizational and social paradigm, and the knowledge-based, ontological paradigm -and give examples of how these paradigms can be used in multi-robot systems. We then look at a common problem in multirobot systems -that of task allocation -and show how the solution approach to this problem is very different depending upon the paradigm chosen for abstracting the problem. Our conclusion is that the paradigms are not interchangeable, but rather the selection of the appropriate paradigm is dependent upon the specific constraints and requirements of the application of interest. Further work is needed to provide guidance to the system designer on selecting the proper abstraction, or paradigm, for a given problem.

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Emotion-based control of cooperating heterogeneous mobile robots

Cited by 78 publications

References 29 publications

Emotions: from brain to robot

Emotions: from brain to robot

The roles of the amygdala in the affective regulation of body, brain, and behaviour

Distributed intelligence: overview of the field and its application in multi-robot systems

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