Accepting Human-like Avatars in Social and Professional Roles

Humans report perceptions of unease or eeriness as humanoid/android robots and digital avatars approach human-like physical resemblance, a phenomenon alluded by the Uncanny Valley theory. This study extends the discussions on interactions and acceptance of digital avatars with findings from three experiments. In the first, perceptive evaluation of actors in clips from computer-generated animation and a live-action version of the same movie was examined. In the second experiment, we considered short clips with highly realistic digital avatars to measure recognition ability, the extent of eeriness, and specific physical features identified as unreal. The fixation area and pupil size variation recorded using an eye tracker were analyzed to infer attention to the body, face, and emotional response, respectively. Building on these findings, the third experiment looked at acceptance in roles requiring human skill, empathy, and cognitive ability. The results show that based on perceptions from physical attributes, the eeriness scores diverge from the uncanny valley theory as human-likeness increases. The realistic CGI and mocap technology could have helped cross the valley. Visual attention inferred from gaze behavior was similar for live-action and CGI. At the same time, we observe pupil size changes reflecting emotions like eeriness when the avatars either talked or smiled. Proficiency and acceptance scores were lower for roles requiring complex social cognition processes, such as friends and judicial decision-making. Interestingly, real-life stereotypes of gender roles were transferred to digital avatars too. The findings suggest an ambiguity in accepting human-like avatars in social and professional interactions, emphasizing the need for a multi-dimensional approach when applying the uncanny valley theory. A detailed and contextual examination is imperative as technological advancements have placed humans closer to co-existing with digital or physical android/humanoid robots.

Impacts of service robots on service quality

With rapid advances in technologies, especially in artificial intelligence, smart sensors, big data analytics, and robotics, the service industry began introducing robots to perform a variety of functions. While the main purpose of deploying robots has been productivity improvement, the current COVID-19 pandemic has brought more urgent purpose, providing contactless service for social distancing. This study explores the service quality provided by robots based on real data in a hotel setting. A sample of 201 guests provided their expected service quality by robots and the actual performance experience after the service. We analyzed this relationship using importance performance analysis (IPA) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The results revealed that customers’ top priorities for robots’ service quality are assurance and reliability, while tangible and empathy were not as important. Customers were not satisfied with robots’ responsiveness, but this construct was found to be a low priority.

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

Attention capability is an essential component of human-robot interaction. Several robot attention models have been proposed which aim to enable a robot to identify the attentiveness of the humans with which it communicates and gives them its attention accordingly. However, previous proposed models are often susceptible to noisy observations and result in the robot's frequent and undesired shifts in attention. Furthermore, most approaches have difficulty adapting to change in the number of participants. To address these limitations, a novel attentiveness determination algorithm is proposed for determining the most attentive person, as well as prioritizing people based on attentiveness. The proposed algorithm, which is based on relevance theory, is named the Scalable Hidden Markov Model (Scalable HMM). The Scalable HMM allows effective computation and contributes an adaptation approach for human attentiveness; unlike conventional HMMs, Scalable HMM has a scalable number of states and observations and online adaptability for state transition probabilities, in terms of changes in the current number of states, i.e., the number of participants in a robot's view. The proposed approach was successfully tested on image sequences (7567 frames) of individuals exhibiting a variety of actions (speaking, walking, turning head, and entering or leaving a robot's view). From these experimental results, Scalable HMM showed a detection rate of 76% in determining the most attentive person and over 75% in prioritizing people's attention with variation in the number of participants. Compared to recent attention approaches, Scalable HMM's performance in people attention prioritization presents an approximately 20% improvement.

Language Use in Joint Action: The Means of Referring Expressions

This study examined how human–human collaboration can be achieved through an exchange of verbal information in exchanging information about the referents in a joint action. Knowing other people’s referential intention is fundamental for joint action. Joint action can be achieved verbally by two types of referring expressions, namely, symbolic and deictic referring expressions. Using corpus data, we extracted nouns as typical symbolic references and demonstratives as typical deictic references. We examined whether the word usage of these terms changed when the robot vehicles controlled by the participants repeatedly performed the same collaborative task. We used a novel virtual space for the task because we wanted to control the common ground shared by the participants. The results of the performance indicate that the task completion became more efficient as the participants repeated the task. The referential word use was reduced in both symbolic and deictic references, and this reduction occurred with a grounding process among the collaborators. The study showed that reduction of referential expressions occurs with the grounding process in human–human collaboration and suggests that appropriate collaborative robot systems must deal with the reduction process of referencing in humans.

Towards Rehabilitation Robotics: Off-the-Shelf BCI Control of Anthropomorphic Robotic Arms

Advances in neural interfaces have demonstrated remarkable results in the direction of replacing and restoring lost sensorimotor function in human patients. Noninvasive brain-computer interfaces (BCIs) are popular due to considerable advantages including simplicity, safety, and low cost, while recent advances aim at improving past technological and neurophysiological limitations. Taking into account the neurophysiological alterations of disabled individuals, investigating brain connectivity features for implementation of BCI control holds special importance. Off-the-shelf BCI systems are based on fast, reproducible detection of mental activity and can be implemented in neurorobotic applications. Moreover, social Human-Robot Interaction (HRI) is increasingly important in rehabilitation robotics development. In this paper, we present our progress and goals towards developing off-the-shelf BCI-controlled anthropomorphic robotic arms for assistive technologies and rehabilitation applications. We account for robotics development, BCI implementation, and qualitative assessment of HRI characteristics of the system. Furthermore, we present two illustrative experimental applications of the BCI-controlled arms, a study of motor imagery modalities on healthy individuals' BCI performance, and a pilot investigation on spinal cord injured patients' BCI control and brain connectivity. We discuss strengths and limitations of our design and propose further steps on development and neurophysiological study, including implementation of connectivity features as BCI modality.

BROMETH: Methodology to design safe reconfigurable medical robotic systems

BACKGROUND

This research paper deals with the development of a medical robotized control system for supracondylar humeral fracture treatment. Concurrent access to shared resources and applying reconfiguration scenarios can jeopardize the safety of the system.

METHODS

A new methodology is proposed in this paper, termed BROMETH, to guarantee the safety of such critical systems from their specification to their deployment, and passing through certification and implementation. The solution is applied to a real case study named Browser-based Reconfigurable Orthopedic Surgery (abbrev. BROS), a robotized platform dedicated to the treatment of supracondylar fractures, to illustrate the paper's contribution. This work starts from a medical issue, namely supracondylar humeral fracture treatment, to establish a new informatics solution, namely a new methodology to design safe reconfigurable medical robotic systems.

RESULTS

The results of the experiments performed on real SCH fracture radiographies were quite satisfactory.

CONCLUSIONS

Clinical experiments can then be performed after deploying the system on real hardware.