Ethological modeling and architecture for an entertainment robot

Arkin, Ronald C.; Fujita, Masahiro; Takagi, Tsuyoshi; Hasegawa, Rika

doi:10.1109/robot.2001.932592

Cited by 60 publications

(32 citation statements)

References 6 publications

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“…We believe that this is a more natural means of emotion recognition, in that the influence of emotion on facial expression or speech can be suppressed relatively easily, and emotional status is inherently reflected in the activity of the nervous system. In the field of psychophysiology, traditional tools for the investigation of human emotional status are based on the recording and statistical analysis of physiological signals from both the central and autonomic nervous systems [8].…”

Section: Related Workmentioning

confidence: 99%

Human Emotion Recognition System

Singh¹

2012

IJIGSP

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Abstract-This paper discusses the application of feature extraction of facial expressions with combination of neural network for the recognition of different facial emotions (happy, sad, angry, fear, surprised, neutral etc..). Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. This paper analyses the limitations with existing system Emotion recognition using brain activity. In this paper by using an existing simulator I have achieved 97 percent accurate results and it is easy and simplest way than Emotion recognition using brain activity system. Purposed system depends upon human face as we know face also reflects the human brain activities or emotions. In this paper neural network has been used for better results. In the end of paper comparisons of existing Human Emotion Recognition System has been made with new one.

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Section: Related Workmentioning

confidence: 99%

Human Emotion Recognition System

Singh¹

2012

IJIGSP

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“…The architecture autonomously selects behaviors based on a "homeostasis regulation rule" [13]. Namely, behaviors are selected to maintain predefined internal states within proper ranges.…”

Section: Situated Behavior Layermentioning

confidence: 99%

Multi-method learning and assimilation

Takamuku

Arkin

2007

Robotics and Autonomous Systems

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Considering the wide range of possible behaviors to be acquired for domestic robots, applying a single learning method is clearly insufficient. In this paper, we propose a new strategy for behavior acquisition for domestic robots where the behaviors are acquired using multiple differing learning methods that are subsequently incorporated into a common behavior selection system, enabling them to be performed in appropriate situations. An example implementation of this strategy applied to the entertainment humanoid robot QRIO is introduced and the results are discussed.

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“…The 1980s saw the emergence of a radically different, ethological approach to robotic agent design, spearheaded by Brooks' subsumption architecture [4] and the mantra "the world is its own best model". Most notable among modern ethological robots is Sony Corporation's lovable robotic dog, AIBO [2], which illustrates both the strengths (operation in dynamic/unpredictable environments) and the weaknesses (inability to reason about goals) of the strict ethological approach. Hybrid architectures, containing both deliberative and reactive components, first appeared in the late 1980s.…”

Section: Related Workmentioning

confidence: 99%

The Cognitive Controller: A Hybrid, Deliberative/Reactive Control Architecture for Autonomous Robots

Qureshi¹,

Terzopoulos²,

Gillett³

2004

Innovations in Applied Artificial Intelligence

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Abstract. The Cognitive Controller (CoCo) is a new, three-tiered control architecture for autonomous agents that combines reactive and deliberative components. A behaviour-based reactive module ensures that the agent can gracefully handle the various real-time challenges of its environment, while a logic-based deliberative module endows the agent with the ability to "think ahead", performing more complex high-level tasks that require planning. A plan execution and monitoring module establishes an advisor-client relationship between the deliberative and reactive modules. We demonstrate CoCo in the context of space robotics-specifically the control of a vision-guided robotic manipulator that can autonomously capture a free-flying satellite in the presence of unforeseen mission anomalies.

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Ethological modeling and architecture for an entertainment robot

Cited by 60 publications

References 6 publications

Human Emotion Recognition System

Human Emotion Recognition System

Multi-method learning and assimilation

The Cognitive Controller: A Hybrid, Deliberative/Reactive Control Architecture for Autonomous Robots

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