Task-oriented conversational agent self-learning based on sentiment analysis

One of the biggest issues in creating a task-oriented conversational agent with natural language processing based on machine learning comes from size and correctness of the training dataset. It could take months or even years of data collection and the resulting static resource may get soon out of date thus requiring a significant amount of work to supervise it. To overcome these difficulties, we implemented an algorithm with the ability of improving learning efficiency based on the emotions and reactions arising from the conversation between a user and the bot, automatically and in real time. To this end, we have studied an error function that, as in any closed loop control system, corrects the input to improve the output. The proposed method is based on both calibrating the interpretation given to the initial dataset and expanding the dictionary with new terms. Thanks to this innovative approach, the satisfaction of the interlocutors is higher if compared to algorithms with a static dataset or with semi-automatic self-learning rules

HRI Users' Studies in the Context of the SciRoc Challenge: Some Insights on Gender-Based Differences

In this paper, we present the outcomes of the first user study designed and evaluated in the context of the Smart City Robotics Challenge (SciRoc Challenge). The study presented in this paper has the main novelty of having been devised and implemented in a realistic environment: a robot competition where robot tasks were developed by participant teams, robots were fully autonomous, and user questionnaires were part of the competition score. Specifically, our study was performed over a scenario configured to instruct a robot to take an elevator of a shopping mall asking for customers support. Leveraging the dedicated questionnaire designed for the tested scenario, we validated the experimental hypothesis if user perception of robots' behaviour may be influenced by the user's gender. In the end, we discuss the results of our study

Therapeutic educational robot enhancing social interactions in the management of obesity

Obesity is a chronic multifactorial pathology determined by many factors, including incorrect eating habits and a low level of physical activity. There is an urgent need to promote a persistent change in lifestyle in obese subjects, but very few individuals maintain long-term results achieved after diet therapies. Therapeutic Education (TE) has taken over an important role as a multidisciplinary intervention aimed at improving lifestyle and at acquiring new skills for the management of the disease. However, only a small portion of patients can maintain participation in such programs and fully benefit from them. Assistive technologies, and in particular assistive social robots, are powerful tools to boost independence and improve participation in educational activities. The aim of the research work described in this article is to evaluate the effect of employing a social robot as a therapeutic educational robot helping the expert therapist in the education activity. This article describes the implementation, deployment, and evaluation of a social educational robot used as a TE assistant. Although we cannot provide statistically significant results due to the limited number of people involved in the experimental protocol, all experimental results show a positive trend, indicating that the robot can enhance the social interactions between the patients and the therapist and among the patients, thus bringing to better overall results of the TE sessions, measured with standard tests for obesity management

From Commands to Goal-based Dialogs: A Roadmap to Achieve Natural Language Interaction in RoboCup@Home

On the one hand, speech is a key aspect to people's communication. On the other, it is widely acknowledged that language proficiency is related to intelligence. Therefore, intelligent robots should be able to understand, at least, people's orders within their application domain. These insights are not new in RoboCup@Home, but we lack of a long-term plan to evaluate this approach. In this paper we conduct a brief review of the achievements on automated speech recognition and natural language understanding in RoboCup@Home. Furthermore, we discuss main challenges to tackle in spoken human-robot interaction within the scope of this competition. Finally, we contribute by presenting a pipelined road map to engender research in the area of natural language understanding applied to domestic service robotics.Comment: 12 pages, 2 tables, 1 figure. Accepted and presented (poster) in the RoboCup 2018 Symposium. In pres

A distributed vision system for boat traffic monitoring in the venice grand canal

Motion detection and Tracking, Distribuited surveillance, Boat traffic monitoring In this paper we describe a system for boat traffic monitoring that has been realized for analyzing and computing statistics of trafic in the Grand Canal in Venice. The system is based on a set of survey cells to monitor about 6 Km of canal. Each survey cell contains three cameras oriented in three directions and covering about 250-300 meters of the canal. This paper presents the segmentation and tracking phases that are used to detect and track boats in the channel and experimental evaluation of the system showing the effectiveness of the approach in the required tasks.

Human Attention Assessment Using A Machine Learning Approach with GAN-based Data Augmentation Technique Trained Using a Custom Dataset

Human–robot interactions require the ability of the system to determine if the user is paying attention. However, to train such systems, massive amounts of data are required. In this study, we addressed the issue of data scarcity by constructing a large dataset (containing ~120,000 photographs) for the attention detection task. Then, by using this dataset, we established a powerful baseline system. In addition, we extended the proposed system by adding an auxiliary face detection module and introducing a unique GAN-based data augmentation technique. Experimental results revealed that the proposed system yields superior performance compared to baseline models and achieves an accuracy of 88% on the test set. Finally, we created a web application for testing the proposed model in real time