Fuzzy Computing for Communication of A Partner Robot Based on Imitation

Kubota, Naoyuki; Nishida, Kazuhiro

doi:10.1109/robot.2005.1570794

Cited by 27 publications

(25 citation statements)

References 11 publications

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“…As a future work, we intend to incorporate the method for extracting human evaluation proposed in our previous works [21,31] for the humanlike partner robot. As another future work, we will introduce a visual system using two CCD cameras for getting more essential information in communication with the human.…”

Section: Discussionmentioning

confidence: 99%

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Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot

et al. 2005

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The purpose of this study is to develop partner robots that can obtain and accumulate human-friendly behaviors. To achieve this purpose, the entire architecture of the robot is designed, based on a concept of structured learning which emphasizes the importance of interactive learning of several modules through interaction with its environment. This paper deals with a trajectory planning method for generating hand-to-hand behaviors of a partner robot by using multiple fuzzy state-value functions, a self-organizing map, and an interactive genetic algorithm. A trajectory for the behavior is generated by an interactive genetic algorithm using human evaluation. In order to reduce human load, human evaluation is estimated by using the fuzzy state-value function. Furthermore, to cope with various situations, a self-organizing map is used for clustering a given task dependent on a human hand position. And then, a fuzzy state-value function is assigned to each output unit of the self-organizing map. The robot can easily obtain and accumulate human-friendly trajectories using a fuzzy state-value function and a knowledge database corresponding to the unit selected in the selforganizing map. Finally, multiple fuzzy state-value functions can estimate a human evaluation model for the hand-to-hand behaviors. Several experimental results show the effectiveness of the proposed method.

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Section: Discussionmentioning

confidence: 99%

“…Therefore, we apply SOM for clustering target points sequentially. As one of unsupervised learning methods, SOM is often used for extracting a relationship among inputs data, since SOM can learn the hidden topological structure from the learning data [29][30][31].…”

Section: Self-organizing Map For Clustering Hand-to-hand Behaviorsmentioning

confidence: 99%

Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot

et al. 2005

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“…We developed two different types of partner robots; a human-like robot called Hubot [6] and a mobile PC called MOBiMac [7] in order to realize the social communication with humans. MOBiMac is composed of two units used for PC and robotic behavior control (Fig.2).…”

Section: A Hardware Architecturementioning

confidence: 99%

“…The robot has two servo motors, four ultrasonic sensors, four light sensors, a microphone, and a CCD camera. The basic behaviors of the robot are visual tracking, map building, human classification, gesture recognition, and voice recognition [6,7,[22][23][24].…”

Section: A Hardware Architecturementioning

confidence: 99%

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Visual Perception for Emotion-Based Communication of a Partner Robot

Kubota

Liu

Mori

2009

KANSEI Engineering International

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This paper discusses natural communication based on an emotional model for partner robots based on visual perception. The partner robots should have various capabilities of perceiving, acting, communicating, and surviving through physically and emotionally interacting with a human. First of all, we discuss the role of an emotional model for a partner robot. Next, we propose the concept of natural communication with an emotional model. Partner robots performs utterances based on the results of the emotional model. Furthermore, this paper proposes a method for extracting human facial landmarks to realize natural communication for partner robots. Evolutionary computation is used for human face detection and human tracking based on adaptive color template matching. A temporary template pattern is updated according to the search result of the previous image. After constructing a normalized human face image, the positions and features of facial landmarks are extracted. Finally, we show experimental results of the emotional communication based on the visual perception.

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Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination

Kubota

Multi-Objective Machine Learning

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Summary. This chapter discusses the behavioral learning of robots from the viewpoint of multiobjective design. Various coordination methods for multiple behaviors have been proposed to improve the control performance and to manage conflicting objectives. We proposed various learning methods for neuro-fuzzy controllers based on evolutionary computation and reinforcement learning. First, we introduce the supervised learning method and evolutionary learning method for multiobjective design of robot behaviors. Then, the multiobjective design of fuzzy spiking neural networks for robot behaviors is presented. The key point behind these methods is to realize the adaptability and reusability of behaviors through interactions with the environment.

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Fuzzy Computing for Communication of A Partner Robot Based on Imitation

Cited by 27 publications

References 11 publications

Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot

Multiple fuzzy state-value functions for human evaluation through interactive trajectory planning of a partner robot

Visual Perception for Emotion-Based Communication of a Partner Robot

Multi-Objective Design of Neuro-Fuzzy Controllers for Robot Behavior Coordination

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