Online Object Searching by a Humanoid Robot in an Unknown Environment

As part of “intelligence,” the “awareness” is the state or ability to perceive, feel, or be mindful of events, objects, or sensory patterns: in other words, to be conscious of the surrounding environment and its interactions. Inspired by early-ages human skills developments and especially by early-ages awareness maturation, the present paper accosts the robots intelligence from a different slant directing the attention to combining both “cognitive” and “perceptual” abilities. Within such a slant, the machine (robot) shrewdness is constructed on the basis of a multilevel cognitive concept attempting to handle complex artificial behaviors. The intended complex behavior is the autonomous discovering of objects by robot exploring an unknown environment: in other words, proffering the robot autonomy and awareness in and about unknown backdrop.

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Revision on fuzzy artificial potential field for humanoid robot path planning in unknown environment

International Journal of Advanced Mechatronic Systems ◽

10.1504/ijamechs.2015.072707 ◽

2015 ◽

Vol 6 (4) ◽

pp. 174 ◽

Cited By ~ 11

Author(s):

Mahdi Fakoor ◽

Amirreza Kosari ◽

Mohsen Jafarzadeh

Keyword(s):

Path Planning ◽

Potential Field ◽

Humanoid Robot ◽

Artificial Potential Field ◽

Robot Path Planning ◽

Unknown Environment ◽

Robot Path

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Online Object Searching with Humanoid Robot by 3D-SLAM and 6DoF Object Detection

The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) ◽

10.1299/jsmermd.2020.2a2-g06 ◽

2020 ◽

Vol 2020 (0) ◽

pp. 2A2-G06

Author(s):

Masato TSURU ◽

Adrien ESCANDE ◽

Kevin CHAPPELET ◽

Arnaud TANGUY ◽

Kensuke HARADA

Keyword(s):

Object Detection ◽

Humanoid Robot ◽

3D Slam ◽

Object Searching

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Fuzzy Q-learning obstacle avoidance algorithm of humanoid robot in unknown environment

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8483654 ◽

2018 ◽

Cited By ~ 1

Author(s):

Shuhuan Wen ◽

Jianhua Chen ◽

Zhen Li ◽

Ahmad B. Rad ◽

Kamal Mohammed Othman

Keyword(s):

Obstacle Avoidance ◽

Humanoid Robot ◽

Q Learning ◽

Unknown Environment

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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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