(1) Background: Conventional shoe manufacturing involves many processes that most workers avoid because of loud noises and harmful environments. Therefore, a robot-based shoe manufacturing system is needed to implement an automated process. (2) Aim: We propose a new robot-based shoe manufacturing automation system that includes an automatic robotic solution for replacing the manual manufacturing processes of the upper and sole. (3) Methods: For the upper manufacturing process, a new trajectory acquisition system with a digitizer and a shoe last turning device is proposed. A method to plan the robot’s tool path for roughing and cementing by industrial robot manipulators is also presented. For the sole manufacturing process, we adopted an industrial robot manipulator with a 3-D scanning system and a cementing tool. A trajectory generation algorithm for cementing the outer and inner sides of the sole by transforming 3-D information of the sole into a 6-D posture for the robot is proposed. (4) Results: All developed systems and proposed algorithms are applied to an automated production testbed, and their performances are experimentally verified. (5) Conclusions: The proposed system and methods can be applied for upper and sole manufacturing processes according to evaluation experiments in a demonstrative production line.

Objective: The purpose of our study is to develop a spoken dialogue system (SDS) for pain questionnaire in patients with spinal disease. We evaluate user satisfaction and validated the performance accuracy of the SDS in medical staff and patients.Methods: The SDS was developed to investigate pain and related psychological issues in patients with spinal diseases based on the pain questionnaire protocol. We recognized patients’ various answers, summarized important information, and documented them. User satisfaction and performance accuracy were evaluated in 30 potential users of SDS, including doctors, nurses, and patients and statistically analyzed.Results: The overall satisfaction score of 30 patients was 5.5 ± 1.4 out of 7 points. Satisfaction scores were 5.3 ± 0.8 for doctors, 6.0 ± 0.6 for nurses, and 5.3 ± 0.5 for patients. In terms of performance accuracy, the number of repetitions of the same question was 13, 16, and 33 (13.5%, 16.8%, and 34.7%) for doctors, nurses, and patients, respectively. The number of errors in the summarized comment by the SDS was 5, 0, and 11 (5.2%, 0.0%, and 11.6 %), respectively. The number of summarization omissions was 7, 5, and 7 (7.3%, 5.3%, and 7.4%), respectively.Conclusion: This is the first study in which voice-based conversational artificial intelligence (AI) was developed for a spinal pain questionnaire and validated by medical staff and patients. The conversational AI showed favorable results in terms of user satisfaction and performance accuracy. Conversational AI can be useful for the diagnosis and remote monitoring of various patients as well as for pain questionnaires in the future.

Dialogue systems must understand children’s utterance intentions by considering their unique linguistic characteristics, such as syntactic incompleteness, pronunciation inaccuracies, and creative expressions, to enable natural conversational engagement in child–robot interactions. Even state-of-the-art large language models (LLMs) for language understanding and contextual awareness cannot comprehend children’s intent as accurately as humans because of their distinctive features. An LLM-based dialogue system should acquire the manner by which humans understand children’s speech to enhance its intention reasoning performance in verbal interactions with children. To this end, we propose a fine-tuning methodology that utilizes the LLM–human judgment discrepancy and interactive response data. The former data represent cases in which the LLM and human judgments of the contextual appropriateness of a child’s answer to a robot’s question diverge. The latter data involve robot responses suitable for children’s utterance intentions, generated by the LLM. We developed a fine-tuned dialogue system using these datasets to achieve human-like interpretations of children’s utterances and to respond adaptively. Our system was evaluated through human assessment using the Robotic Social Attributes Scale (RoSAS) and Sensibleness and Specificity Average (SSA) metrics. Consequently, it supports the effective interpretation of children’s utterance intentions and enables natural verbal interactions, even in cases with syntactic incompleteness and mispronunciations.

The home robot-based child activity service aims to cultivate children’s social emotions. A design theme was produced by interviewing child development experts and parents. The activity service is composed of 50 plays and 70 conversations. These were developed based on activities from psychomotor therapy and the guidelines of Ministry of Early Childhood Education in South Korea. In the field test, 50 children aged five–seven years participated to experience the activity services at home for 4 days. After completing the 4 days of field testing, we conducted customer satisfaction (CSAT) surveys, Godspeed evaluations and interviews to quantitatively and qualitatively verify the evaluations by the children and parents. As a result, 92% of the children and 80% of the parents evaluated that they were satisfied with the service. In addition, our results revealed that the social robot-based service contributed to improving the relationship between children and families by functioning as a messenger. Finally, the lessons learned from the service development and field tests were discussed to aid service designers and robotics engineers.