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Robotics
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Chatbots and Talking Robots
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Chatbots and Talking Robots

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "AI in Robotics".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 4437

Special Issue Editors

Dr. Soyeon Caren Han

E-Mail Website
Guest Editor
School of Computer Science, The University of Sydney, Sydney, Australia
Interests: natural language processing with deep learning
Dr. Naira Hovakimyan

E-Mail Website
Guest Editor
Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Room 223, 1206 West Green Street, Urbana, IL 61801, USA
Interests: control and optimization; autonomous systems; machine learning; neural networks, game theory, and their applications in aerospace, robotics, mechanical, agricultural, electrical, petroleum, biomedical engineering, and elderly care
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, with advances in artificial intelligence and machine learning, we have witnessed the rapid growth of robots.  They have been developed to provide services through a wide range of methods for humankind.  Thanks to the development of natural language processing and large language models, chatbots and talking robots have slowly become more similar to humans, and have started to physically interact with humans. They are widely employed in various scenarios, such as medical and financial applications, active assisted living, social companionship, cooperative work and even distance education.

Based on these previous statements, this Special Issue has been established to cover the recent trends and advances in chatbots and talking robots.  We are looking for state-of-the-art research that covers topics related to chatbots, including, but not limited to, the following:

  • Natural language processing;
  • Human–robot interactions;
  • Advanced technologies used in chatbots and talking robots;
  • AI in chatbots.

Dr. Soyeon Caren Han
Prof. Dr. Naira Hovakimyan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Robotics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (3 papers)

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Research

20 pages, 2340 KiB  
Article
Comparative Analysis of Generic and Fine-Tuned Large Language Models for Conversational Agent Systems
by Laura Villa, David Carneros-Prado, Cosmin C. Dobrescu, Adrián Sánchez-Miguel, Guillermo Cubero and Ramón Hervás
Robotics 2024, 13(5), 68; https://doi.org/10.3390/robotics13050068 - 29 Apr 2024
Viewed by 335
Abstract
In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the [...] Read more.
In the rapidly evolving domain of conversational agents, the integration of Large Language Models (LLMs) into Chatbot Development Platforms (CDPs) is a significant innovation. This study compares the efficacy of employing generic and fine-tuned GPT-3.5-turbo models for designing dialog flows, focusing on the intent and entity recognition crucial for dynamic conversational interactions. Two distinct approaches are introduced: a generic GPT-based system (G-GPT) leveraging the pre-trained model with complex prompts for intent and entity detection, and a fine-tuned GPT-based system (FT-GPT) employing customized models for enhanced specificity and efficiency. The evaluation encompassed the systems’ ability to accurately classify intents and recognize named entities, contrasting their adaptability, operational efficiency, and customization capabilities. The results revealed that, while the G-GPT system offers ease of deployment and versatility across various contexts, the FT-GPT system demonstrates superior precision, efficiency, and customization, although it requires initial training and dataset preparation. This research highlights the versatility of LLMs in enriching conversational features for talking assistants, from social robots to interactive chatbots. By tailoring these advanced models, the fluidity and responsiveness of conversational agents can be enhanced, making them more adaptable and effective in a variety of settings, from customer service to interactive learning environments. Full article
(This article belongs to the Special Issue Chatbots and Talking Robots)
34 pages, 2639 KiB  
Article
The Co-Design of an Embodied Conversational Agent to Help Stroke Survivors Manage Their Recovery
by Deborah Richards, Paulo Sergio Miranda Maciel and Heidi Janssen
Robotics 2023, 12(5), 120; https://doi.org/10.3390/robotics12050120 - 22 Aug 2023
Cited by 1 | Viewed by 1475
Abstract
Whilst the use of digital interventions to assist patients with self-management involving embodied conversational agents (ECA) is emerging, the use of such agents to support stroke rehabilitation and recovery is rare. This iTakeCharge project takes inspiration from the evidence-based narrative style self-management intervention [...] Read more.
Whilst the use of digital interventions to assist patients with self-management involving embodied conversational agents (ECA) is emerging, the use of such agents to support stroke rehabilitation and recovery is rare. This iTakeCharge project takes inspiration from the evidence-based narrative style self-management intervention for stroke recovery, the ‘Take Charge’ intervention, which has been shown to contribute to significant improvements in disability and quality of life after stroke. We worked with the developers and deliverers of the ‘Take Charge’ intervention tool, clinical stroke researchers and stroke survivors, to adapt the ‘Take Charge’ intervention tool to be delivered by an ECA (i.e., the Taking Charge Intelligent Agent (TaCIA)). TaCIA was co-designed using a three-phased approach: Stage 1: Phase I with the developers and Phase II with people who delivered the original Take Charge intervention to stroke survivors (i.e., facilitators); and Stage 2: Phase III with stroke survivors. This paper reports the results from each of these phases including an evaluation of the resulting ECA. Stage 1: Phase I, where TaCIA V.1 was evaluated by the Take Charge developers, did not build a good working alliance, provide adequate options, or deliver the intended Take Charge outcomes. In particular, the use of answer options and the coaching aspects of TaCIA V.1 were felt to conflict with the intention that Take Charge facilitators would not influence the responses of the patient. In response, in Stage 1: Phase II, TaCIA V.2 incorporated an experiment to determine the value of providing answer options versus free text responses. Take Charge facilitators agreed that allowing an open response concurrently with providing answer options was optimal and determined that working alliance and usability were satisfactory. Finally, in Stage 2: Phase III, TaCIA V.3 was evaluated with eight stroke survivors and was generally well accepted and considered useful. Increased user control, clarification of TaCIA’s role, and other improvements to improve accessibility were suggested. The article concludes with limitations and recommendations for future changes based on stroke survivor feedback. Full article
(This article belongs to the Special Issue Chatbots and Talking Robots)
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18 pages, 5020 KiB  
Article
SceneGATE: Scene-Graph Based Co-Attention Networks for Text Visual Question Answering
by Feiqi Cao, Siwen Luo, Felipe Nunez, Zean Wen, Josiah Poon and Soyeon Caren Han
Robotics 2023, 12(4), 114; https://doi.org/10.3390/robotics12040114 - 07 Aug 2023
Cited by 1 | Viewed by 1708
Abstract
Visual Question Answering (VQA) models fail catastrophically on questions related to the reading of text-carrying images. However, TextVQA aims to answer questions by understanding the scene texts in an image–question context, such as the brand name of a product or the time on [...] Read more.
Visual Question Answering (VQA) models fail catastrophically on questions related to the reading of text-carrying images. However, TextVQA aims to answer questions by understanding the scene texts in an image–question context, such as the brand name of a product or the time on a clock from an image. Most TextVQA approaches focus on objects and scene text detection, which are then integrated with the words in a question by a simple transformer encoder. The focus of these approaches is to use shared weights during the training of a multi-modal dataset, but it fails to capture the semantic relations between an image and a question. In this paper, we proposed a Scene Graph-Based Co-Attention Network (SceneGATE) for TextVQA, which reveals the semantic relations among the objects, the Optical Character Recognition (OCR) tokens and the question words. It is achieved by a TextVQA-based scene graph that discovers the underlying semantics of an image. We create a guided-attention module to capture the intra-modal interplay between the language and the vision as a guidance for inter-modal interactions. To permit explicit teaching of the relations between the two modalities, we propose and integrate two attention modules, namely a scene graph-based semantic relation-aware attention and a positional relation-aware attention. We conduct extensive experiments on two widely used benchmark datasets, Text-VQA and ST-VQA. It is shown that our SceneGATE method outperforms existing ones because of the scene graph and its attention modules. Full article
(This article belongs to the Special Issue Chatbots and Talking Robots)
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