Recognition of Confusing Objects for NAO Robot

Author(s):  
Thanh-Long Nguyen ◽  
Didier Coquin ◽  
Reda Boukezzoula
Keyword(s):  
Author(s):  
Megan Strait ◽  
Florian Lier ◽  
Jasmin Bernotat ◽  
Sven Wachsmuth ◽  
Friederike Eyssel ◽  
...  
Keyword(s):  

Robotica ◽  
2020 ◽  
pp. 1-14
Author(s):  
Chen Hao ◽  
Liu Chengju ◽  
Chen Qijun

SUMMARY Self-localization in highly dynamic environments is still a challenging problem for humanoid robots with limited computation resource. In this paper, we propose a dual-channel unscented particle filter (DC-UPF)-based localization method to address it. A key novelty of this approach is that it employs a dual-channel switch mechanism in measurement updating procedure of particle filter, solving for sparse vision feature in motion, and it leverages data from a camera, a walking odometer, and an inertial measurement unit. Extensive experiments with an NAO robot demonstrate that DC-UPF outperforms UPF and Monte–Carlo localization with regard to accuracy.


10.28945/4749 ◽  
2021 ◽  
Vol 20 ◽  
pp. 245-261
Author(s):  
Mariam Alawi Alhashmi ◽  
Omar Mubin ◽  
Rama Bassam Baroud

Aim/Purpose: This study sought to understand the views of both teachers and students on the usage of humanoid robots as teaching assistants in a specifically Arab context. Background: Social robots have in recent times penetrated the educational space. Although prevalent in Asia and some Western regions, the uptake, perception and acceptance of educational robots in the Arab or Emirati region is not known. Methodology: A total of 20 children and 5 teachers were randomly selected to comprise the sample for this study, which was a qualitative exploration executed using focus groups after an NAO robot (pronounced now) was deployed in their school for a day of revision sessions. Contribution: Where other papers on this topic have largely been based in other countries, this paper, to our knowledge, is the first to examine the potential for the integration of educational robots in the Arab context. Findings: The students were generally appreciative of the incorporation of humanoid robots as co-teachers, whereas the teachers were more circumspect, expressing some concerns and noting a desire to better streamline the process of bringing robots to the classroom. Recommendations for Practitioners: We found that the malleability of the robot’s voice played a pivotal role in the acceptability of the robot, and that generally students did well in smaller groups with the robot; teachers expressed concern that the children would become easily distracted should too many children be privy to one robot. Recommendation for Researchers: Our results provide valuable recommendations for researchers in the area. We believe, there needs to be continued efforts in devising suitable methodological assessment tools to evaluate student and teacher attitudes in the classroom particularly in the Arab world. We also advise researchers to focus on providing adaptive behavior in the context of educational robots. There are different distinct areas that need further clarifications and study based on our review. Impact on Society: On a wider scale, the findings of this paper have a huge implication for the educational technology as the integration of robotics in education is one of the emerging trends in the area, particularly in the UAE. This study allows to answer questions related to attitudes and perceptions of both teachers and students toward educational robots in the UAE. Future Research: Possible avenues of research in the area include focusing on the adaptive and natural behavior of robots in disciplines other than Mathematics as a means of successfully integrating robots in the classroom.


Author(s):  
Eman Alarfaj ◽  
Hissah Alabdullatif ◽  
Huda Alabdullatif ◽  
Ghazal Albakri ◽  
Nor Shahriza Abdul Karim

Author(s):  
Stanislav Ondos ◽  
Matus Pleva ◽  
Radovan Kristan ◽  
Rastislav Husovsky ◽  
Jozef Juhar
Keyword(s):  

Author(s):  
Till Halbach ◽  
Trenton Schulz ◽  
Wolfgang Leister ◽  
Ivar Solheim

We transformed the existing learning program Language Shower, which is used in some Norwegian day-care centers in the Grorud district of Oslo municipality, into a digital solution using an app for smartphone or tablet with the option for further enhancement of presentation by a NAO robot. The solution was tested in several iterations and multiple day-care centers over several weeks. Measurements of the children’s progress across learning sessions indicate a positive impact of the program using a robot as compared to the program without robot. In-situ observations and interviews with day care center staff confirmed the solution’s many advantages, but also revealed some important areas for improvement. In particular, the speech recognition needs to be more flexible and robust, and special measures have to be in place to handle children speaking simultaneously.


2019 ◽  
Vol 7 (1) ◽  
pp. 318 ◽  
Author(s):  
Octavian Melinte ◽  
Luige Vladareanu ◽  
Ionel-Alexandru Gal

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6438
Author(s):  
Chiara Filippini ◽  
David Perpetuini ◽  
Daniela Cardone ◽  
Arcangelo Merla

An intriguing challenge in the human–robot interaction field is the prospect of endowing robots with emotional intelligence to make the interaction more genuine, intuitive, and natural. A crucial aspect in achieving this goal is the robot’s capability to infer and interpret human emotions. Thanks to its design and open programming platform, the NAO humanoid robot is one of the most widely used agents for human interaction. As with person-to-person communication, facial expressions are the privileged channel for recognizing the interlocutor’s emotional expressions. Although NAO is equipped with a facial expression recognition module, specific use cases may require additional features and affective computing capabilities that are not currently available. This study proposes a highly accurate convolutional-neural-network-based facial expression recognition model that is able to further enhance the NAO robot’ awareness of human facial expressions and provide the robot with an interlocutor’s arousal level detection capability. Indeed, the model tested during human–robot interactions was 91% and 90% accurate in recognizing happy and sad facial expressions, respectively; 75% accurate in recognizing surprised and scared expressions; and less accurate in recognizing neutral and angry expressions. Finally, the model was successfully integrated into the NAO SDK, thus allowing for high-performing facial expression classification with an inference time of 0.34 ± 0.04 s.


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