A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings

2022 ◽  
Vol 73 ◽  
pp. 102231
Author(s):  
Debasmita Mukherjee ◽  
Kashish Gupta ◽  
Li Hsin Chang ◽  
Homayoun Najjaran
Mechatronics ◽  
2018 ◽  
Vol 55 ◽  
pp. 248-266 ◽  
Author(s):  
Valeria Villani ◽  
Fabio Pini ◽  
Francesco Leali ◽  
Cristian Secchi

2016 ◽  
pp. 135-142 ◽  
Author(s):  
JOSÉ DE GEA FERNÉNDEZ ◽  
HOLGER SPRENGEL ◽  
MARTIN MALLWITZ ◽  
MICHAEL ZIPPER ◽  
BINGBIN YU ◽  
...  

2022 ◽  
Vol 62 ◽  
pp. 28-43
Author(s):  
Ana Correia Simões ◽  
Ana Pinto ◽  
Joana Santos ◽  
Sofia Pinheiro ◽  
David Romero

2017 ◽  
Vol 94 ◽  
pp. 102-119 ◽  
Author(s):  
José de Gea Fernández ◽  
Dennis Mronga ◽  
Martin Günther ◽  
Tobias Knobloch ◽  
Malte Wirkus ◽  
...  

Robotics ◽  
2013 ◽  
pp. 1328-1353 ◽  
Author(s):  
Artur M. Arsénio

This chapter presents work on developmental machine learning strategies applied to robots for language acquisition. The authors focus on learning by scaffolding and emphasize the role of the human caregiver for robot learning. Indeed, language acquisition does not occur in isolation, neither can it be a robot’s “genetic legacy.” Rather, they propose that language is best acquired incrementally, in a social context, through human-robot interactions in which humans guide the robot, as if it were a child, through the learning process. The authors briefly discuss psychological models related to this work and describe and discuss computational models that they implemented for robot language acquisition. The authors aim to introduce robots into our society and treat them as us, using child development as a metaphor for robots’ developmental language learning.


Author(s):  
Artur M. Arsénio

This chapter presents work on developmental machine learning strategies applied to robots for language acquisition. The authors focus on learning by scaffolding and emphasize the role of the human caregiver for robot learning. Indeed, language acquisition does not occur in isolation, neither can it be a robot’s “genetic legacy.” Rather, they propose that language is best acquired incrementally, in a social context, through human-robot interactions in which humans guide the robot, as if it were a child, through the learning process. The authors briefly discuss psychological models related to this work and describe and discuss computational models that they implemented for robot language acquisition. The authors aim to introduce robots into our society and treat them as us, using child development as a metaphor for robots’ developmental language learning.


2016 ◽  
Vol 75 (3) ◽  
pp. 123-132 ◽  
Author(s):  
Marie Crouzevialle ◽  
Fabrizio Butera

Abstract. Performance-approach goals (i.e., the desire to outperform others) have been found to be positive predictors of test performance, but research has also revealed that they predict surface learning strategies. The present research investigates whether the high academic performance of students who strongly adopt performance-approach goals stems from test anticipation and preparation, which most educational settings render possible since examinations are often scheduled in advance. We set up a longitudinal design for an experiment conducted in high-school classrooms within the context of two science, technology, engineering, and mathematics (STEM) disciplines, namely, physics and chemistry. First, we measured performance-approach goals. Then we asked students to take a test that had either been announced a week in advance (enabling strategic preparation) or not. The expected interaction between performance-approach goal endorsement and test anticipation was moderated by the students’ initial level: The interaction appeared only among low achievers for whom the pursuit of performance-approach goals predicted greater performance – but only when the test had been scheduled. Conversely, high achievers appeared to have adopted a regular and steady process of course content learning whatever their normative goal endorsement. This suggests that normative strivings differentially influence the study strategies of low and high achievers.


Sign in / Sign up

Export Citation Format

Share Document