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Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them. We address these issues by introducing a knowledge representation scheme to facilitate skill generalization and explainability. This scheme allows representing knowledge of the robot’s understanding of a scene and performed task. We also present techniques for extracting this knowledge from raw data. Such knowledge representation and extraction methods have not been explored adequately in previous research. Our approach does not require any prior knowledge or 3D models of the objects involved. Moreover, the representation scheme is easy to understand for humans. The system is modular so that new recognition or reasoning routines can be added without changing the basic architecture. We developed a computer vision system and a task reasoning module that works with our knowledge representation. The efficacy of our approach is demonstrated with two different tasks: hanging items on pegs and stacking one item on another. A formalization of our knowledge representation scheme is presented, showing how the system is capable of learning from a few demonstrations.

In recent years, there have been many approaches to using robots to teach computer programming. In intelligent tutoring systems and computer-aided learning, there is also some research to show that affective feedback to the student increases learning efficiency. However, a few studies on the role of incorporating an emotional personality in the robot in robot-assisted learning have found different results. To explore this issue further, we conducted a pilot study to investigate the effect of positive verbal encouragement and non-verbal emotive behaviour of the Miro-E robot during a robot-assisted programming session. The participants were tasked to program the robot’s behaviour. In the experimental group, the robot monitored the participants’ emotional state via their facial expressions, and provided affective feedback to the participants after completing each task. In the control group, the robot responded in a neutral way. The participants filled out a questionnaire before and after the programming session. The results show a positive reaction of the participants to the robot and the exercise. Though the number of participants was small, as the experiment was conducted during the pandemic, a qualitative analysis of the data was carried out. We found that the greatest affective outcome of the session was for students who had little experience or interest in programming before. We also found that the affective expressions of the robot had a negative impact on its likeability, revealing vestiges of the uncanny valley effect.

The cold-start scenario is a critical problem for recommendation systems, especially in dynamically changing domains such as online news services. In this research, we aim at addressing the cold-start situation by adapting an unsupervised neural User2Vec method to represent new users and articles in a multidimensional space. Toward this goal, we propose an extension of the Doc2Vec model that is capable of representing users with unknown history by building embeddings of their metadata labels along with item representations. We evaluate our proposed approach with respect to different parameter configurations on three real-world recommendation datasets with different characteristics. Our results show that this approach may be applied as an efficient alternative to the factorization machine-based method when the user and item metadata are used and hence can be applied in the cold-start scenario for both new users and new items. Additionally, as our solution represents the user and item labels in the same vector space, we can analyze the spatial relations among these labels to reveal latent interest features of the audience groups as well as possible data biases and disparities.

We describe here our design and implementation of a cyborg insect, called CameraRoach, with onboard camera feedback that can be navigated via remote control providing a first-person view. The camera pack is mounted on the Madagascar hissing cockroach, which is small enough to fit into crevices but also can carry a printed circuit boards with power, communication, and sensor components (visual camera). For navigating the cockroach, we implemented a unique electronic backpack neural stimulator, which allows the cockroach to be maneuvered on a desired path with a joystick. A high-resolution wireless camera, also included in the backpack, sends live images via a WiFi (Wireless Fidelity) network. We present the results of an evaluation experiment with the CameraRoach and compare it with the other state of the art systems like the Beetle-Cam.

We present here an approach to the deployment of social robots in a science laboratory to monitor the behavior of students with respect to safety regulations to prevent accidents. Our vision is that the social robot should act as a friendly companion for students and encourage them to follow safe laboratory practices. Towards this goal, we developed a Laboratory Safety Assistant Framework (LSA) using a Misty II Plus robot and designed three dashboards within it as interventions. This LSA framework was evaluated using a participatory design (PD) study with twenty university students (eleven from Japan and nine from Egypt). For this study, we designed a questionnaire that contains 42 questions on the prior knowledge of students about socially assistive robots and their expectations about how socially assistive robots can create a secure environment in the scientific laboratory. The chi-square test revealed that there are no differences between groups in their perceptions of using Misty II to achieve safety inside science laboratories. In their perception of the capabilities of social robots and the sharing of feelings, students believe that using social robots like Misty II inside the science laboratory can make the lab safe and decrease risk inside the science laboratory without using the three dashboards of the LSA framework. However, the Wilcoxon signed-rank test revealed that there is a significant improvement in students’ perceptions ((Median = 106.5, Z = −2.39, p < 0.05, r = 0.53)) between students’ expectations of using social robots to achieve safety in scientific laboratories before and after they interacted with the social robot and knew about the feasibility of the three dashboards we designed. Furthermore, the t-test revealed participants’ experiences of sharing feelings with a social robot, and the intervention suggested by the LSA framework was to design a system integrating this into a social robot to enhance safety within the scientific laboratory (t (19) = 3.39, p = 0.003).

This paper introduces a novel VGG-16-based visual navigation architecture for a differential drive robot, Social Plantroid, using a temporal vision mosaic, a novel approach which joins current and previous robot vision frames for heading estimation. As a minor contribution, it integrates a novel sunlight/shadow detection algorithm using Gabor filters. The neural network is trained with simulated data employing the artificial potential field method, which is another novelty for map-less robot navigation. Virtual and real-world experiments validate the effectiveness of this architecture in obstacle avoidance and navigation.

As alternative remote communication interfaces become increasingly common, ensuring that they seamlessly integrate into daily life has become a pressing design challenge. In this context, what should a huggable communication device look like—should it have arms or a face, or resemble a conventional pillow? This study investigates users’ preferences and personalities regarding the appearance of such interfaces for remote emotional interaction. As a case study, we present HugBits, a round, cushion-like device that transmits hugs through visual and tactile feedback. Drawing on the prior literature and a participatory design workshop, we developed seven shape variations and evaluated them through an online survey with 79 Polish participants. The results reveal a consistent preference for less anthropomorphic designs, with users valuing comfort, simplicity, and intuitive affordances such as areas to rest the head or wrap the arms around. Although personality traits did not significantly predict preferences, the findings highlight broader design criteria: huggable communication interfaces, intended to remain visible and available in shared spaces, must balance emotional expressiveness with social acceptability. These insights provide guidelines for designing emotionally engaging, user-centered mediated touch technologies.

We present a unified method to augmented incremental recognition for online handwritten Japanese and English text, which is used for busy or on-the-fly recognition while writing, and lazy or delayed recognition after writing, without incurring long waiting times. It extends the local context for segmentation and recognition to a range of recent strokes called “segmentation scope” and “recognition scope,” respectively. The recognition scope is inside of the segmentation scope. The augmented incremental recognition triggers recognition at every several recent strokes, updates the segmentation and recognition candidate lattice, and searches over the lattice for the best result incrementally. It also incorporates three techniques. The first is to reuse the segmentation and recognition candidate lattice in the previous recognition scope for the current recognition scope. The second is to fix undecided segmentation points if they are stable between character/word patterns. The third is to skip recognition of partial candidate character/word patterns. The augmented incremental method includes the case of triggering recognition at every new stroke with the above-mentioned techniques. Experiments conducted on TUAT-Kondate and IAM online database show its superiority to batch recognition (recognizing text at one time) and pure incremental recognition (recognizing text at every input stroke) in processing time, waiting time, and recognition accuracy.

Gestures play an integral role in human communication. Our research aims to develop a gesture understanding system that allows for better interpretation of human instructions in household robotics settings. We conducted an experiment with 34 participants who used pointing gestures to teach concepts to an assistant. Gesture data were analyzed using manual annotations (MAXQDA) and the computational methods of pose estimation and k-means clustering. The study revealed that participants tend to maintain consistent pointing styles, with one-handed pointing and index finger gestures being the most common. Gaze and pointing often co-occur, as do leaning forward and pointing. Using our gesture categorization algorithm, we analyzed gesture information values. As the experiment progressed, the information value of gestures remained stable, although the trends varied between participants and were associated with factors such as age and gender. These findings underscore the need for gesture recognition systems to balance generalization with personalization for more effective human–robot interaction.

We conducted a comparative study to evaluate the efficacy of synchronous and asynchronous interaction modes when providing feedback for improving academic writing, achievement motivation and critical thinking. The participants undertook an academic writing task, and were given feedback by a mentor using one of three interaction modes: asynchronous, synchronous e-feedback, and face to face. Data was collected from the participants via an assessment rubric applied to their writing after each stage of interaction, along with pretest and posttest questionnaires, and a semi-structured interview at the end. We found that synchronous feedback is more effective in increasing the quality of academic writing and achievement motivation, but for critical thinking we did not find any significant difference. We suggest that a strategy combining the advantages of each mode of interaction, considering the writers’ experience, may be the most effective way to promote academic writing, achievement motivation and critical thinking.