Disentanglement in conceptual space during sensorimotor interaction

Zhong, Junpei; Ogata, Tetsuya; Cangelosi, Angelo; Yang, Chenguang

doi:10.1049/ccs.2019.0007

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…A recent review of the literature by Marchetti et al (2018) showed that different physical characteristics of HSRs may significantly affect the quality of interaction between humans and robots at different ages. The construction of robots that integrate and expand the specific biological abilities of our species led to two different directions in robotic development based on different, though related, theoretical perspectives: developmental cybernetics (DC; Itakura, 2008 ; Itakura et al, 2008 ; Moriguchi et al, 2011 ; Kannegiesser et al, 2015 ; Okanda et al, 2018 ; Di Dio et al, 2019 ; Wang et al, 2020 ; Manzi et al, 2020a ) and developmental robotics (DR; De La Cruz et al, 2014 ; Cangelosi and Schlesinger, 2015 , 2018 ; Lyon et al, 2016 ; Morse and Cangelosi, 2017 ; Vinanzi et al, 2019 ; Zhong et al, 2019 ; Di Dio et al, 2020a , b ). The first perspective (DC) consists of creating a human-like system, by simulating human psychological processes and prosthetic functions in the robot (enhancing the function and lifestyle of persons) to observe people’s behavioral response toward the robot.…”

Section: Introductionmentioning

confidence: 99%

A Robot Is Not Worth Another: Exploring Children’s Mental State Attribution to Different Humanoid Robots

et al. 2020

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Recent technological developments in robotics has driven the design and production of different humanoid robots. Several studies have highlighted that the presence of human-like physical features could lead both adults and children to anthropomorphize the robots. In the present study we aimed to compare the attribution of mental states to two humanoid robots, NAO and Robovie, which differed in the degree of anthropomorphism. Children aged 5, 7, and 9 years were required to attribute mental states to the NAO robot, which presents more human-like characteristics compared to the Robovie robot, whose physical features look more mechanical. The results on mental state attribution as a function of children’s age and robot type showed that 5-year-olds have a greater tendency to anthropomorphize robots than older children, regardless of the type of robot. Moreover, the findings revealed that, although children aged 7 and 9 years attributed a certain degree of human-like mental features to both robots, they attributed greater mental states to NAO than Robovie compared to younger children. These results generally show that children tend to anthropomorphize humanoid robots that also present some mechanical characteristics, such as Robovie. Nevertheless, age-related differences showed that they should be endowed with physical characteristics closely resembling human ones to increase older children’s perception of human likeness. These findings have important implications for the design of robots, which also needs to consider the user’s target age, as well as for the generalizability issue of research findings that are commonly associated with the use of specific types of robots.

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Section: Introductionmentioning

confidence: 99%

A Robot Is Not Worth Another: Exploring Children’s Mental State Attribution to Different Humanoid Robots

et al. 2020

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“…Therefore, our method is highly compatible with conventional self-supervised learning focusing on geometric problems, and when combined with conventional methods, it is expected to enable more complex and dynamic behaviors, depending on the environment and situation. In recent years, research has been conducted to understand concepts such as color and shape in language and images from the physicality of the robot using selfsupervised learning [55]. Further development of the proposed method may lead to the understanding of complex and abstract concepts correlated with speed, such as "fast or slow," "strong or weak," and "heavy or light," through the dynamic body movements of the robot.…”

Section: ) Resultsmentioning

confidence: 99%

Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

2022

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Robots are expected to replace menial tasks such as housework. Some of these tasks include nonprehensile manipulation performed without grasping objects. Nonprehensile manipulation is very difficult because it requires considering the dynamics of environments and objects. Imitating complex behaviors requires a large number of human demonstrations. In this study, a self-supervised learning that considers motion speed to achieve variable speed for nonprehensile manipulation is proposed. The proposed method collects and fine-tunes only successful actions among the data obtained during autonomous operations. By fine-tuning the successful data using speed labels, the robot learns the dynamics among itself, its environment, and objects. We experimented with the task of scooping and transporting pancakes using the neural network model trained on 24 human-collected training data. The proposed method significantly improved the success rate from 40.2% to 85.7%, and succeeded more than 75% for other objects.

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“…A recent review of the literature by showed that different physical characteristics of HSRs may significantly affect the quality of interaction between humans and robots at different ages. The construction of robots that integrate and expand the specific biological abilities of our species led to two different directions in robotic development based on different, though related, theoretical perspectives: developmental cybernetics (DC; Itakura, 2008;Moriguchi et al, 2011;Kannegiesser et al, 2015;Okanda et al, 2018;Wang et al, 2020;Manzi et al, 2020a) and developmental robotics (DR;De La Cruz et al, 2014;Schlesinger, 2015, 2018;Lyon et al, 2016;Morse and Cangelosi, 2017;Zhong et al, 2019;Di Dio et al, 2020a,b). The first perspective (DC) consists of creating a human-like system, by simulating human psychological processes and prosthetic functions in the robot (enhancing the function and lifestyle of persons) to observe people's behavioral response toward the robot.…”

Section: Introductionmentioning

confidence: 99%

Sociomateriality in Children with Typical and/or Atypical Development

Iannaccone¹,

Savarese²,

Manzi³

et al. 2021

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Disentanglement in conceptual space during sensorimotor interaction

Cited by 3 publications

References 24 publications

A Robot Is Not Worth Another: Exploring Children’s Mental State Attribution to Different Humanoid Robots

A Robot Is Not Worth Another: Exploring Children’s Mental State Attribution to Different Humanoid Robots

Imitation Learning for Nonprehensile Manipulation Through Self-Supervised Learning Considering Motion Speed

Sociomateriality in Children with Typical and/or Atypical Development

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