3P2-P03 Object Manipulation Behavior Recognition of a Humanoid Robot using a Sensory-motor Integration Mechanism by Deep Neural Network(Neurorobotics & Cognitive Robotics)

The present study examines the possible roles of cortical chaos in generating novel actions for achieving specified goals. The proposed neural network model consists of a sensory-forward model responsible for parietal lobe functions, a chaotic network model for premotor functions and prefrontal cortex model responsible for manipulating the initial state of the chaotic network. Experiments using humanoid robot were performed with the model and showed that the action plans for satisfying specific novel goals can be generated by diversely modulating and combining prior-learned behavioral patterns at critical dynamical states. Although this criticality resulted in fragile goal achievements in the physical environment of the robot, the reinforcement of the successful trials was able to provide a substantial gain with respect to the robustness. The discussion leads to the hypothesis that the consolidation of numerous sensory-motor experiences into the memory, meditating diverse imagery in the memory by cortical chaos, and repeated enaction and reinforcement of newly generated effective trials are indispensable for realizing an open-ended development of cognitive behaviors.

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Binary Neural Network in Robotic Manipulation: Flexible Object Manipulation for Humanoid Robot Using Partially Binarized Auto-Encoder on FPGA

10.1109/iros51168.2021.9636825 ◽

2021 ◽

Author(s):

Satoshi Ohara ◽

Tetsuya Ogata ◽

Hiromitsu Awano

Keyword(s):

Neural Network ◽

Humanoid Robot ◽

Object Manipulation ◽

Robotic Manipulation ◽

Binary Neural Network

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Is it useful for a robot to visit a museum?

Paladyn Journal of Behavioral Robotics ◽

10.1515/pjbr-2018-0025 ◽

2018 ◽

Vol 9 (1) ◽

pp. 374-390

Author(s):

Aliaa Moualla ◽

Sofiane Boucenna ◽

Ali Karaouzene ◽

Denis Vidal ◽

Philippe Gaussier

Keyword(s):

Neural Network ◽

Humanoid Robot ◽

Emotional Responses ◽

Biologically Inspired ◽

Museum Visitors ◽

Unknown Environment ◽

Individual Preferences ◽

Sensory Motor ◽

Special Environment ◽

Two Populations

AbstractIn this work, we study how learning in a special environment such as a museum can influence the behavior of robots. More specifically, we show that online learning based on interaction with people at a museum leads the robots to develop individual preferences. We first developed a humanoid robot (Berenson) that has the ability to head toward its preferred object and to make a facial expression that corresponds to its attitude toward said object. The robot is programmed with a biologically-inspired neural network sensory-motor architecture. This architecture allows Berenson to learn and to evaluate objects. During experiments, museum visitors’ emotional responses to artworks were recorded and used to build a database for training. A similar database was created in the laboratory with laboratory objects. We use those databases to train two simulated populations of robots. Each simulated robot emulates the Berenson sensory-motor architecture. Firstly, the results show the good performance of our architecture in artwork recognition in the museum. Secondly, they demonstrate the effect of training variability on preference diversity. The response of the two populations in a new unknown environment is different; the museum population of robots shows a greater variance in preferences than the population of robots that have been trained only on laboratory objects. The obtained diversity increases the chances of success in an unknown environment and could favor an accidental discovery.

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Research on behavior recognition based on feature fusion of automatic coder and recurrent neural network

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189290 ◽

2020 ◽

Vol 39 (6) ◽

pp. 8927-8935

Author(s):

Bing Zheng ◽

Dawei Yun ◽

Yan Liang

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Behavior Pattern ◽

Rapid Development ◽

Video Data ◽

Support Vector ◽

Behavior Recognition ◽

Learning Methods ◽

The Impact ◽

Internet Of Things Technology

Under the impact of COVID-19, research on behavior recognition are highly needed. In this paper, we combine the algorithm of self-adaptive coder and recurrent neural network to realize the research of behavior pattern recognition. At present, most of the research of human behavior recognition is focused on the video data, which is based on the video number. At the same time, due to the complexity of video image data, it is easy to violate personal privacy. With the rapid development of Internet of things technology, it has attracted the attention of a large number of experts and scholars. Researchers have tried to use many machine learning methods, such as random forest, support vector machine and other shallow learning methods, which perform well in the laboratory environment, but there is still a long way to go from practical application. In this paper, a recursive neural network algorithm based on long and short term memory (LSTM) is proposed to realize the recognition of behavior patterns, so as to improve the accuracy of human activity behavior recognition.

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