ABSTRACT Animating realistic avatars requires using high‐quality animations for every possible state the avatar can be in. This includes actions like walking or running, but also subtle movements that convey emotions and personality. Idle animations, such as standing, breathing, or looking around, are crucial for realism and believability. In virtual applications, these are often handcrafted or recorded with actors, but this is costly. Furthermore, recording realistic idle animations may be complex, since the actor being aware of the recording could interfere with the genuineness of the movements. Currently, there are no large‐scale idle animation datasets for deep learning, and this recording challenge may partly explain this. Nevertheless, this paper concludes that both acted and genuine idle animations are perceived as real, and users are not able to distinguish between them. It also states that raw recorded idle animations and artist‐retouched ones are perceived differently. These conclusions mean that recording idle animations should be easier than expected, implying that actors can be instructed to act the movements, significantly simplifying the recording process. This should help future efforts to record idle animation datasets. Finally, we publish ReActIdle, the first three dimensional idle animation dataset containing long sequences of real and acted idle motions.

As robot technology advances, collaboration between humans and robots will become more prevalent in industrial tasks. When humans run into issues in such scenarios, a likely future involves relying on artificial agents or robots for aid. This study identifies key aspects for the design of future user-assisting agents. We analyze quantitative and qualitative data from a user study examining the impact of on-demand assistance received from a remote human in a human-robot collaboration (HRC) assembly task. We study scenarios in which users require help and we assess their experiences in requesting and receiving assistance. Additionally, we investigate participants’ perceptions of future non-human assisting agents and whether assistance should be on-demand or unsolicited. Through a user study, we analyze the impact that such design decisions (human or artificial assistant, on-demand or unsolicited help) can have on elicited emotional responses, productivity, and preferences of humans engaged in HRC tasks.

In an industrial production context, when a robotic arm is assigned a new task, it must be re-programmed by specialized personnel with the necessary skills and knowledge. In addition, the robot remains offline while being re-programmed, negatively affecting productivity. Mixed reality opens up the opportunity for a production worker with no programming skills to teach a robot by interacting with the hologram of its digital twin, thus not interfering with the physical robot during re-programming. We describe the design and implementation of a mixed-reality interface to instruct trajectories for a robot end-effector by moving the hologram of its digital twin by hand. The interface supports the integrality of this interaction so that the user can configure the end effector location and orientation simultaneously. We also report a user study (n = 14) to characterize this interface in terms of usability, user experience, and estimated temporal efficiency that it can provide when programming robot trajectories.

Natural gesturing is very important for the credibility of social robots. It is even more crucial for storytelling robots since the expression, emotion and emphasis must be highlighted. In this paper we propose a hybrid gesture generation approach for a storytelling robot that combines beats automatically generated by a GAN with a probabilistic semantic related gesture insertion system. Beats are executed according to a probability based on the duration of the sentences and semantic gesture insertions are dependent of the previous occurrences of the gestures associated to the words. The polarity of the text is extracted and affects several features of the motion to arouse emotion. A qualitative evaluation of robot behavior is conducted and confirms the approach as a promising one as storytelling system.

Abstract Natural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation .

This paper presents a novel approach for iris dissimilarity computation based on Machine Learning paradigms and Computer Vision transformations. Based on the training dataset given by the MICHE II Challenge organizers, a set of classifiers has been constructed and tested, aiming at classifying a single image. The main novelty of this paper remains in the used approach to iris dissimilarity computation: given two iris images, both of them are classified using the same paradigm, obtaining the a posteriori probability for each of the considered class values. Hence, two distributions are obtained, one for each iris image, and the dissimilarity is computed as the distance between these two distributions. Experimental results indicate the appropriateness of this new approach, even though more research and experiments are needed to obtain some improvements and to accelerate the classification process.

Bertsolaritza, Basque improvised contest poetry, offers another sphere to develop robot body language and robot communication capabilities, that shares some similarities with theatrical performances. It is also a new area to work on social robotics. The work presented in this paper makes some steps forward in designing and implementing the set of behaviors the robots need to show in the stage to increase, on the one hand robot autonomy and on the other hand, credibility and sociability.

We describe a robot capable of composing and playing traditional Basque impromptu verses - bertsoak. The system, called Bertsobot, is able to construct improvised verses according to given constraints on rhyme and meter, and to perform it in public. Towards this end, several tools and applications have been developed and integrated in Bertsobot, including: speech-based communication system, text applications for verse generation, and robot behaviours to interact with the environment in a public performance. We describe the tools and processes behind our approach, present some early experimental results and illustrative verses, and finally, remark the conclusions and future steps.

The K Nearest Neighbors classification method assigns to an unclassified observation the class which obtains the best results after a voting criteria is applied among the observation's K nearest, previously classified points. In a validation process the optimal K is selected for each database and all the cases are classified with this K value. However the optimal K for the database does not have to be the optimal K for all the points. In view of that, we propose a new version where the K value is selected dynamically. The new unclassified case is classified with different K values. And looking for each K how many votes has obtained the winning class, we select the class of the most reliable one. To calculate the reliability, we use the Positive Predictive Value (PPV) that we obtain after a validation process. The new algorithm is tested on several datasets and it is compared with the K-Nearest Neighbor rule.