Target Positioning and Grasping of NAO Robot Based on Monocular Stereo Vision

With the continuous advancement of science and technology and the rapid development of robotics, it has become an inevitable trend for domestic robots to enter thousands of households. In order to solve the inconvenience problem of the elderly and people with special needs, because the elderly and other people in need may need the help of domestic robots due to inconvenient legs and feet, the research of the robot target position based on monocular stereo vision and the understanding of the robot NAO are very important. Research and experiments are carried out on the target recognition and positioning in the process of NAO robot grasping. This paper proposes a recognition algorithm corresponding to quantitative component statistical information. First, extract the area of interest that contains the purpose from the image. After that, to eliminate interference in various fields and achieve target recognition, the robot cameras have almost no common field of view and can only use one camera at the same time. Therefore, this article uses the monocular vision principle to locate the target, and the detection algorithm is based on the structure of the robot head material, establishes the relationship between the height change of the machine head and the tilt angle, and improves the monocular vision NAO robot detection algorithm. According to experiments, the accuracy of the robot at close range can be controlled below 1 cm. This article completes the robot’s grasping and transmission of the target. About 80% of the external information that humans can perceive comes from vision. In addition, there are advantages such as high efficiency and good stability.

Expression Recognition Method Using Improved VGG16 Network Model in Robot Interaction

Aiming at the problems of poor representation ability and less feature data when traditional expression recognition methods are applied to intelligent applications, an expression recognition method based on improved VGG16 network is proposed. Firstly, the VGG16 network is improved by using large convolution kernel instead of small convolution kernel and reducing some fully connected layers to reduce the complexity and parameters of the model. Then, the high-dimensional abstract feature data output by the improved VGG16 is input into the convolution neural network (CNN) for training, so as to output the expression types with high accuracy. Finally, the expression recognition method combined with the improved VGG16 and CNN model is applied to the human-computer interaction of the NAO robot. The robot makes different interactive actions according to different expressions. The experimental results based on CK + dataset show that the improved VGG16 network has strong supervised learning ability. It can extract features well for different expression types, and its overall recognition accuracy is close to 90%. Through multiple tests, the interactive results show that the robot can stably recognize emotions and make corresponding action interactions.

Pitch It Right: Using Prosodic Entrainment to Improve Robot-Assisted Foreign Language Learning in School-Aged Children

Robot-assisted language learning (RALL) is a promising application when employing social robots to help both children and adults acquire a language and is an increasingly widely studied area of child–robot interaction. By introducing prosodic entrainment, i.e., converging the robot’s pitch with that of the learner, the present study aimed to provide new insights into RALL as a facilitative method for interactive tutoring. It is hypothesized that pitch-level entrainment by a Nao robot during a word learning task in a foreign language will result in increased learning in school-aged children. The results indicate that entrainment has no significant effect on participants’ learning, contra the hypothesis. Research on the implementation of entrainment in the context of RALL is new. This study highlights constraints in currently available technologies for voice generation and methodological limitations that should be taken into account in future research.

Robot-Enhanced Language Learning for Children in Norwegian Day-Care Centers

In a case study, 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 smartphones or tablets with the option for further enhancement of the 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 indicated a positive impact of the program using a robot as compared to the program without a 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.

10 Years of Human-NAO Interaction Research: A Scoping Review

The evolving field of human-robot interaction (HRI) necessitates that we better understand how social robots operate and interact with humans. This scoping review provides an overview of about 300 research works focusing on the use of the NAO robot from 2010 to 2020. This study presents one of the most extensive and inclusive pieces of evidence on the deployment of the humanoid NAO robot and its global reach. Unlike most reviews, we provide both qualitative and quantitative results regarding how NAO is being used and what has been achieved so far. We analyzed a wide range of theoretical, empirical, and technical contributions that provide multidimensional insights, such as general trends in terms of application, the robot capabilities, its input and output modalities of communication, and the human-robot interaction experiments that featured NAO (e.g. number and roles of participants, design, and the length of interaction). Lastly, we derive from the review some research gaps in current state-of-the-art and provide suggestions for the design of the next generation of social robots.

Improving Human–Robot Interaction by Enhancing NAO Robot Awareness of Human Facial Expression

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.

Robot-Enhanced Language Learning for Children in Norwegian Day-Care Centers

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.