Invariant EKF based 2D Active SLAM with Exploration Task

Virtual reality (VR) in retailing (V-commerce) has been proven to enhance the consumer experience. Thus, this technology is beneficial to study behavioral patterns by offering the opportunity to infer customers’ personality traits based on their behavior. This study aims to recognize impulsivity using behavioral patterns. For this goal, 60 subjects performed three tasks—one exploration task and two planned tasks—in a virtual market. Four noninvasive signals (eye-tracking, navigation, posture, and interactions), which are available in commercial VR devices, were recorded, and a set of features were extracted and categorized into zonal, general, kinematic, temporal, and spatial types. They were input into a support vector machine classifier to recognize the impulsivity of the subjects based on the I-8 questionnaire, achieving an accuracy of 87%. The results suggest that, while the exploration task can reveal general impulsivity, other subscales such as perseverance and sensation-seeking are more related to planned tasks. The results also show that posture and interaction are the most informative signals. Our findings validate the recognition of customer impulsivity using sensors incorporated into commercial VR devices. Such information can provide a personalized shopping experience in future virtual shops.

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Predicting the Energy Consumption of a Robot in an Exploration Task Using Optimized Neural Networks

Electronics ◽

10.3390/electronics10080920 ◽

2021 ◽

Vol 10 (8) ◽

pp. 920

Author(s):

Liesle Caballero ◽

Álvaro Perafan ◽

Martha Rinaldy ◽

Winston Percybrooks

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Mobile Robot ◽

Energy Budget ◽

Dynamic Models ◽

Pearson Correlation ◽

Experimental Conditions ◽

Grid Map ◽

Proposed Model ◽

Exploration Task

This paper deals with the problem of determining a useful energy budget for a mobile robot in a given environment without having to carry out experimental measures for every possible exploration task. The proposed solution uses machine learning models trained on a subset of possible exploration tasks but able to make predictions on untested scenarios. Additionally, the proposed model does not use any kinematic or dynamic models of the robot, which are not always available. The method is based on a neural network with hyperparameter optimization to improve performance. Tabu List optimization strategy is used to determine the hyperparameter values (number of layers and number of neurons per layer) that minimize the percentage relative absolute error (%RAE) while maximize the Pearson correlation coefficient (R) between predicted data and actual data measured under a number of experimental conditions. Once the optimized artificial neural network is trained, it can be used to predict the performance of an exploration algorithm on arbitrary variations of a grid map scenario. Based on such prediction, it is possible to know the energy needed for the robot to complete the exploration task. A total of 128 tests were carried out using a robot executing two exploration algorithms in a grid map with the objective of locating a target whose location is not known a priori by the robot. The experimental energy consumption was measured and compared with the prediction of our model. A success rate of 96.093% was obtained, measured as the percentage of tests where the energy budget suggested by the model was enough to actually carry out the task when compared to the actual energy consumed in the test, suggesting that the proposed model could be useful for energy budgeting in actual mobile robot applications.

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Process Design for Autonomous Car Mining 1st Prototype “ACM1PT” to Help on Exploration Task on Outdoor Environments

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.823.447 ◽

2016 ◽

Vol 823 ◽

pp. 447-452

Author(s):

Crhistian Segura ◽

Juan Hernandez ◽

Hoffman Ramirez ◽

Oscar Aviles ◽

Mauricio Mauledoux ◽

...

Keyword(s):

Mechanical Strength ◽

Process Design ◽

Vehicle Suspension ◽

Mining Sector ◽

Whole Process ◽

Outdoor Environments ◽

Robotic Vehicle ◽

The Right ◽

Exploration Task ◽

Selection Of

The follow paper explains the process design of the development of a mobile robotic vehicle which main purpose is to aid on task of exploration on the mining sector. The paper shows the whole process from the customer needs through the conceptual sketch to the definitive design. It also describes mathematical considerations for the selection of the motors for locomotion and steering. Followed by mechanical strength simulations in order to choose the right material and finally simulations of the behavior of the robotic vehicle suspension.

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Tabula-rasa exploration decreases during youth and is linked to ADHD symptoms

10.1101/2020.06.11.146019 ◽

2020 ◽

Author(s):

M Dubois ◽

A Bowler ◽

ME Moses-Payne ◽

J Habicht ◽

N Steinbeis ◽

...

Keyword(s):

Computational Modelling ◽

Health Conditions ◽

Childhood And Adolescence ◽

Developmental Study ◽

Trade Off ◽

Hyperactivity Disorder ◽

Older Adolescents ◽

The World ◽

Exploration Exploitation ◽

Exploration Task

AbstractDuring childhood and adolescence, exploring the unknown is important to build a better model of the world. This means that youths have to regularly solve the exploration-exploitation trade-off, a dilemma in which adults are known to deploy a mixture of computationally light and heavy exploration strategies. In this developmental study, we investigated how youths (aged 8 to 17) performed an exploration task that allows us to dissociate these different exploration strategies. Using computational modelling, we demonstrate that tabula-rasa exploration, a computationally light exploration heuristic, is used to a higher degree in children and younger adolescents compared to older adolescents. Additionally, we show that this tabula-rasa exploration is more extensively used by youths with high attention-deficit/hyperactivity disorder (ADHD) traits. In the light of ongoing brain development, our findings show that children and younger adolescents use computationally less burdensome strategies, but that an excessive use thereof might be a risk for mental health conditions.

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Is self-exploration task concerning college life meaningful for career adaptation?

The Proceedings of the Annual Convention of the Japanese Psychological Association ◽

10.4992/pacjpa.77.0_2am-030 ◽

2013 ◽

Vol 77 (0) ◽

pp. 2AM-030-2AM-030

Author(s):

Saeko SHITARA ◽

Chiaki KUWABARA

Keyword(s):

College Life ◽

Exploration Task

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Altered inhibition of dentate granule cells during spatial learning in an exploration task

Journal of Neuroscience ◽

10.1523/jneurosci.16-03-01247.1996 ◽

1996 ◽

Vol 16 (3) ◽

pp. 1247-1259 ◽

Cited By ~ 48

Author(s):

EI Moser

Keyword(s):

Spatial Learning ◽

Granule Cells ◽

Dentate Granule Cells ◽

Exploration Task

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Scopolamine and MK-801 impair recognition memory in a new spontaneous object exploration task in monkeys

Pharmacology Biochemistry and Behavior ◽

10.1016/j.pbb.2021.173300 ◽

2021 ◽

pp. 173300

Author(s):

André W.C. Oliveira ◽

Jéssica V.N. Pacheco ◽

Clara S. Costa ◽

Jéssica Aquino ◽

Rafael S. Maior ◽

...

Keyword(s):

Recognition Memory ◽

Mk 801 ◽

Object Exploration ◽

Exploration Task

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Sometimes slower is better: slow-exploring birds are more sensitive to changes in a vocal discrimination task

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2010.1669 ◽

2010 ◽

Vol 278 (1706) ◽

pp. 767-773 ◽

Cited By ~ 109

Author(s):

Lauren M. Guillette ◽

Adam R. Reddon ◽

Marisa Hoeschele ◽

Christopher B. Sturdy

Keyword(s):

Cognitive Task ◽

Parus Major ◽

Task Demands ◽

Close Relative ◽

Poecile Atricapillus ◽

Selection Hypothesis ◽

Fitness Advantage ◽

Operant Discrimination ◽

Learning Tasks ◽

Exploration Task

Animal personality, defined as consistent individual differences across context and time, has attracted much recent research interest in the study of animal behaviour. More recently, this field has begun to examine how such variation arose and is maintained within populations. The habitat-dependent selection hypothesis, which posits that animals with differing personality types may fare better (i.e. have a fitness advantage) in different habitats, suggests one possible mechanism. In the current experiment, we tested whether slow- and fast-exploring black-capped chickadees ( Poecile atricapillus ), determined by performance in a novel environment exploration task, perform differentially when the demands of an acoustic operant discrimination (cognitive) task were altered following successful task acquisition. We found that slow-exploring birds learn to reverse previously learned natural category rules more quickly than faster exploring conspecifics. In accordance with the habitat-dependent selection hypothesis, and previous work with great tits ( Parus major ), a close relative of the black-capped chickadee, our results suggest that fast-exploring birds may perform better in stable, predictable environments where forming a routine is advantageous, while slow-exploring birds are favoured in unstable, unpredictable environments, where task demands often change. Our results also support a hypothesis derived from previous work with great tits; slow-exploring birds may be generally more flexible (i.e. able to modify their behaviour in accordance with changes in environmental stimuli) in some learning tasks.

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