Cognitive robotics for the modelling of cognitive dysfunctions: A study on unilateral spatial neglect

© 2015 IEEE. Damage to the posterior parietal cortex (PPC) can cause patients to fail to orient toward, explore, and respond to stimuli on the contralesional side of the space. PPC is thought to play a crucial role in the computation of sensorimotor transformations that is in linking sensation to action. Indeed, this disorder, known as Unilateral Spatial Neglect (USN), can compromise visual, auditory, tactile, and olfactory modalities and may involve personal, extra-personal, and imaginal space [1], [2]. For this reason, USN describes a collection of behavioural symptoms in which patients appear to ignore, forget, or turn away from contralesional space [3]. Given the complexity of the disease and the difficulties to study human patients affected by USN, because of their impairments, several computer simulation studies were carried out via artificial neural networks in which damage to the connection weights was also found to yield neglect-related behaviour [4]-[6]

Social robots for older users: a possibility to support assessment and social interventions

In the last decades, various researches in the field of robotics have created numerous opportunities for innovative support of the older population. The goal of this work was to review and highlight how social robots can help the daily life of older people, and be useful also as assessment tools. We will underline the aspects of usability and acceptability of robotic supports in the psychosocial work with older persons. The actual usability of the system influences the perception of the ease of use only when the user has no or low experience, while expert users’ perception is related to their attitude towards the robot. This finding should be more deeply analysed because it may have a strong influence on the design of future interfaces for elderly-robot interaction. Robots can play an important role to tackle the societal challenge of the growing older population. The authors report some recent studies with older users, where it was demonstrated that the acceptability of robotics during daily life activities, and also in cognitive evaluation, could be supported by social robot

A Developmental Neuro-Robotics Approach for Boosting the Recognition of Handwritten Digits

Developmental psychology and neuroimaging research identified a close link between numbers and fingers, which can boost the initial number knowledge in children. Recent evidence shows that a simulation of the children's embodied strategies can improve the machine intelligence too. This article explores the application of embodied strategies to convolutional neural network models in the context of developmental neurorobotics, where the training information is likely to be gradually acquired while operating rather than being abundant and fully available as the classical machine learning scenarios. The experimental analyses show that the proprioceptive information from the robot fingers can improve network accuracy in the recognition of handwritten Arabic digits when training examples and epochs are few. This result is comparable to brain imaging and longitudinal studies with young children. In conclusion, these findings also support the relevance of the embodiment in the case of artificial agents’ training and show a possible way for the humanization of the learning process, where the robotic body can express the internal processes of artificial intelligence making it more understandable for humans

Affect Recognition in Autism: a single case study on integrating a humanoid robot in a standard therapy.

Autism Spectrum Disorder (ASD) is a multifaceted developmental disorder that comprises a mixture of social impairments, with deficits in many areas including the theory of mind, imitation, and communication. Moreover, people with autism have difficulty in recognising and understanding emotional expressions. We are currently working on integrating a humanoid robot within the standard clinical treatment offered to children with ASD to support the therapists. In this article, using the A-B-A' single case design, we propose a robot-assisted affect recognition training and to present the results on the child’s progress during the five months of clinical experimentation. In the investigation, we tested the generalization of learning and the long-term maintenance of new skills via the NEPSY-II affection recognition sub-test. The results of this single case study suggest the feasibility and effectiveness of using a humanoid robot to assist with emotion recognition training in children with ASD

Kindergarten Children Attitude Towards Humanoid Robots: what is the Effect of the First Experience?

Possible applications of robots are growing in educational contexts, where they can support and enhance the traditional learning at any level, including kindergarten. However, the acceptance of such novel technology among the kids is not fully understood, especially for the youngest ones. In this abstract, we present an experiment that investigates the attitude of 52 preschooler children before and after the interaction with a humanoid robot in kindergarten setting. The main hypothesis is that ideas and prejudices can change after a controlled interaction with a physical robot. The study found that children exposed to the robot decrease their distress and positively change their attitude toward the technological device. The results suggest that an early, controlled exposure may facilitate future acceptance

A comparison of kindergarten storytelling by human and humanoid robot with different social behavior

In this paper, we present a study on the influence of different social behavior on preschool children's perception of stories narrated either by a humanoid robot or by a human teacher. Four conditions were considered: static human, static robot, expressive human and expressive robot. Two stories, with knowledge and emotional content, were narrated in two different encounters. After each story, children draw what they remember of the story. We examined drawings of 81 children to study whether the sociability of the teacher (robot or human) could influence elements and details recorded. Results suggest a positive effect of the expressive behavior in robot storytelling, whose efficacy is comparable to the human with the same behavior or better if the expressive robot is compared with a static inexpressive human

A Deep Neural Network for Finger Counting and Numerosity Estimation

In this paper, we present neuro-robotics models with a deep artificial neural network capable of generating finger counting positions and number estimation. We first train the model in an unsupervised manner where each layer is treated as a Restricted Boltzmann Machine or an autoencoder. Such a model is further trained in a supervised way. This type of pretraining is tested on our baseline model and two methods of pre-training are compared. The network is extended to produce finger counting positions. The performance in number estimation of such an extended model is evaluated. We test the hypothesis if the subitizing process can be obtained by one single model used also for estimation of higher numerosities. The results confirm the importance of unsupervised training in our enumeration task and show some similarities to human behaviour in the case of subitizing

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table

An embodied model for handwritten digits recognition in a cognitive robot

This paper presents an embodied model for recognition of handwritten digits in a cognitive developmental robot scenario. Inspired by neuro-psychological data, the model integrates three modules: a stacked auto-encoder network to process the visual information, a feedforward neural controller for the fingers, and a generalized regression network that associates number digits to finger configurations. Results from developmental learning experiments show an improvement in the digits' recognition rate thanks to the inclusion of the robot fingers in the training especially in its early stages (epochs) or with a low number of examples. This behaviour can be linked to that observed in psychological studies with children, who seem to benefit of finger counting only in the initial stage of mathematical learning. These results suggest the potential of the embodied approach to favour the creation of a psychologically plausible developmental model for mathematical cognition in robots and to support the creation of more complex models of human-like behaviours

“Robot, tell me a tale!”: A Social Robot as tool for Teachers in Kindergarten

Robots are versatile devices that are promising tools for supporting teaching and learning in the classroom or at home. In fact, robots can be engaging and motivating, especially for young children. This paper presents an experimental study with 81 kindergarten children on memorizations of two tales narrated by a humanoid robot. Variables of the study are the content of the tales (knowledge or emotional) and the different social behaviour of the narrators: static human, static robot, expressive human, and expressive robot. Results suggest a positive effect of the expressive behaviour in robot storytelling, whose effectiveness is comparable to a human with the same behaviour and better when compared with a static inexpressive human. Higher efficacy is achieved by the robot in the tale with knowledge content, while the limited capability to express emotions made the robot less effective in the tale with emotional content