Frontiers in Robotics and AI最新文献

This study proposes a modular platform to improve the adoption of gamification in conventional physical rehabilitation programs. The effectiveness of rehabilitation is correlated to a patient's adherence to the program. This adherence can be diminished due to factors such as motivation, feedback, and isolation. Gamification is a means of adding game-like elements to a traditionally non-game activity. This has been shown to be effective in providing a more engaging experience and improving adherence. The platform is made of three main parts; a central hardware hub, various wired and wireless sensors, and a software program with a stream-lined user interface. The software interface and hardware peripherals were all designed to be simple to use by either a medical specialist or an end-user patient without the need for technical training. A usability study was performed using a group of university students and a group of medical specialists. Using the System Usability Scale, the system received an average score of 69.25 ± 20.14 and 72.5 ± 17.16 by the students and medical specialists, respectively. We also present a framework that attempts to assist in selecting commercial games that are viable for physical rehabilitation.

Force is crucial for learning psychomotor skills in laparoscopic tissue manipulation. Fundamental laparoscopic surgery (FLS), on the other hand, only measures time and position accuracy. FLS is a commonly used training program for basic laparoscopic training through part tasks. The FLS is employed in most of the laparoscopic training systems, including box trainers and virtual reality (VR) simulators. However, many laparoscopic VR simulators lack force feedback and measure tissue damage solely through visual feedback based on virtual collisions. Few VR simulators that provide force feedback have subjective force metrics. To provide an objective force assessment for haptic skills training in the VR simulators, we extend the FLS part tasks to haptic-based FLS (HFLS), focusing on controlled force exertion. We interface the simulated HFLS part tasks with a customized bi-manual haptic simulator that offers five degrees of freedom (DOF) for force feedback. The proposed tasks are evaluated through face and content validity among laparoscopic surgeons of varying experience levels. The results show that trainees perform better in HFLS tasks. The average Likert score observed for face and content validity is greater than 4.6 ± 0.3 and 4 ± 0.5 for all the part tasks, which indicates the acceptance of the simulator among subjects for its appearance and functionality. Face and content validations show the need to improve haptic realism, which is also observed in existing simulators. To enhance the accuracy of force rendering, we incorporated a laparoscopic tool force model into the simulation. We study the effectiveness of the model through a psychophysical study that measures just noticeable difference (JND) for the laparoscopic gripping task. The study reveals an insignificant decrease in gripping-force JND. A simple linear model could be sufficient for gripper force feedback, and a non-linear LapTool force model does not affect the force perception for the force range of 0.5-2.5 N. Further study is required to understand the usability of the force model in laparoscopic training at a higher force range. Additionally, the construct validity of HFLS will confirm the applicability of the developed simulator to train surgeons with different levels of experience.

Introduction: Flow state, the optimal experience resulting from the equilibrium between perceived challenge and skill level, has been extensively studied in various domains. However, its occurrence in industrial settings has remained relatively unexplored. Notably, the literature predominantly focuses on Flow within mentally demanding tasks, which differ significantly from industrial tasks. Consequently, our understanding of emotional and physiological responses to varying challenge levels, specifically in the context of industry-like tasks, remains limited. Methods: To bridge this gap, we investigate how facial emotion estimation (valence, arousal) and Heart Rate Variability (HRV) features vary with the perceived challenge levels during industrial assembly tasks. Our study involves an assembly scenario that simulates an industrial human-robot collaboration task with three distinct challenge levels. As part of our study, we collected video, electrocardiogram (ECG), and NASA-TLX questionnaire data from 37 participants. Results: Our results demonstrate a significant difference in mean arousal and heart rate between the low-challenge (Boredom) condition and the other conditions. We also found a noticeable trend-level difference in mean heart rate between the adaptive (Flow) and high-challenge (Anxiety) conditions. Similar differences were also observed in a few other temporal HRV features like Mean NN and Triangular index. Considering the characteristics of typical industrial assembly tasks, we aim to facilitate Flow by detecting and balancing the perceived challenge levels. Leveraging our analysis results, we developed an HRV-based machine learning model for discerning perceived challenge levels, distinguishing between low and higher-challenge conditions. Discussion: This work deepens our understanding of emotional and physiological responses to perceived challenge levels in industrial contexts and provides valuable insights for the design of adaptive work environments.

Soft grippers are garnering increasing attention for their adeptness in conforming to diverse objects, particularly delicate items, without warranting precise force control. This attribute proves especially beneficial in unstructured environments and dynamic tasks such as food handling. Human hands, owing to their elevated dexterity and precise motor control, exhibit the ability to delicately manipulate complex food items, such as small or fragile objects, by dynamically adjusting their grasping configurations. Furthermore, with their rich sensory receptors and hand-eye coordination that provide valuable information involving the texture and form factor, real-time adjustments to avoid damage or spill during food handling appear seamless. Despite numerous endeavors to replicate these capabilities through robotic solutions involving soft grippers, matching human performance remains a formidable engineering challenge. Robotic competitions serve as an invaluable platform for pushing the boundaries of manipulation capabilities, simultaneously offering insights into the adoption of these solutions across diverse domains, including food handling. Serving as a proxy for the future transition of robotic solutions from the laboratory to the market, these competitions simulate real-world challenges. Since 2021, our research group has actively participated in RoboSoft competitions, securing victories in the Manipulation track in 2022 and 2023. Our success was propelled by the utilization of a modified iteration of our Retractable Nails Soft Gripper (RNSG), tailored to meet the specific requirements of each task. The integration of sensors and collaborative manipulators further enhanced the gripper's performance, facilitating the seamless execution of complex grasping tasks associated with food handling. This article encapsulates the experiential insights gained during the application of our highly versatile soft gripper in these competition environments.

Despite robots being applied in various situations of modern society, some people avoid them or do not feel comfortable interacting with them. Designs that allow robots to interact appropriately with people will make a positive impression on them resulting in a better evaluation of robots, which will solve this problem. To establish such a design, this study conducted two scenario-based experiments focusing on the politeness of the robot's conversation and behavior, and examined the impressions caused when the robot succeeds or slightly fails at a task. These two experiments revealed that regardless of whether the partner is a robot or a human, politeness not only affected the impression of interaction but also the expectations for better task results on the next occasion. Although the effect of politeness on preference toward robot agents was smaller than those toward human agents when agents failed a task, people were more likely to interact with polite robots and human agents again because they thought that they would not fail the next time. This study revealed that politeness motivates people to interact with robots repeatedly even if they make minor mistakes, suggesting that the politeness design is important for encouraging human-robot interaction.

Companion robots are aimed to mitigate loneliness and social isolation among older adults by providing social and emotional support in their everyday lives. However, older adults' expectations of conversational companionship might substantially differ from what current technologies can achieve, as well as from other age groups like young adults. Thus, it is crucial to involve older adults in the development of conversational companion robots to ensure that these devices align with their unique expectations and experiences. The recent advancement in foundation models, such as large language models, has taken a significant stride toward fulfilling those expectations, in contrast to the prior literature that relied on humans controlling robots (i.e., Wizard of Oz) or limited rule-based architectures that are not feasible to apply in the daily lives of older adults. Consequently, we conducted a participatory design (co-design) study with 28 older adults, demonstrating a companion robot using a large language model (LLM), and design scenarios that represent situations from everyday life. The thematic analysis of the discussions around these scenarios shows that older adults expect a conversational companion robot to engage in conversation actively in isolation and passively in social settings, remember previous conversations and personalize, protect privacy and provide control over learned data, give information and daily reminders, foster social skills and connections, and express empathy and emotions. Based on these findings, this article provides actionable recommendations for designing conversational companion robots for older adults with foundation models, such as LLMs and vision-language models, which can also be applied to conversational robots in other domains.