A Bayesian approach to imitation learning for robot navigation

2007 ◽  
Author(s):  
Mark Ollis ◽  
Wesley H. Huang ◽  
Michael Happold
Keyword(s):  
Bayesian Approach ◽  
Robot Navigation ◽  
Imitation Learning

scholarly journals EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning

2019 ◽  
Author(s):  
Kunal Menda ◽  
Katherine Driggs-Campbell ◽  
Mykel J. Kochenderfer
Keyword(s):  
Bayesian Approach ◽  
Imitation Learning

Indoor Mobile Robot Navigation by Central Following Based on Monocular Vision

2009 ◽  
Vol 129 (8) ◽  
pp. 1576-1584 ◽  
Author(s):  
Takeshi Saitoh ◽  
Naoya Tada ◽  
Ryosuke Konishi
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Monocular Vision ◽  
Mobile Robot Navigation ◽  
Indoor Mobile Robot

Assessing Forecast Uncertainty: An Information Bayesian Approach

2017 ◽  
Author(s):  
Fabian Mendez-Ramos
Keyword(s):  
Bayesian Approach ◽  
Forecast Uncertainty

Blind Channel Estimation Using Bayesian Approach for Wireless Communication

2008 ◽  
Vol 2 (3) ◽  
pp. 71-77
Author(s):  
Ramana Reddy M ◽  
Madhavilatha M
Keyword(s):  
Wireless Communication ◽  
Channel Estimation ◽  
Bayesian Approach ◽  
Blind Channel Estimation ◽  
Blind Channel

DEVELOPMENT OF A CONTROL STRATEGY FOR MOBILE ROBOT NAVIGATION IN UNKNOWN TERRAIN

2019 ◽  
Author(s):  
Diego Gabriel Gomes Rosa ◽  
Carlos Luiz Machado de souza junior ◽  
Marco Antonio Meggiolaro ◽  
Luiz Fernando Martha
Keyword(s):  
Mobile Robot ◽  
Control Strategy ◽  
Robot Navigation ◽  
Mobile Robot Navigation

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

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