In this paper, we analyze the possibilities offered by Deep Learning State-of-the-Art architectures such as Transformers and Visual Transformers in generating a prediction of the human’s force in a Human-Robot collaborative object transportation task at a middle distance. We outperform our previous predictor by achieving a success rate of 93.8% in testset and 90.9% in real experiments with 21 volunteers predicting in both cases the force that the human will exert during the next 1 s. A modification in the architecture allows us to obtain a second output from the model with a velocity prediction, which allows us to improve the capabilities of our predictor if it is used to estimate the trajectory that the human-robot pair will follow. An ablation test is also performed to verify the relative contribution to performance of each input.

In this article, we propose a generalization of a Deep Learning State-of-the-Art architecture such as Retentive Networks so that it can accept video sequences as input. With this generalization, we design a force/velocity predictor applied to the medium-distance Human-Robot collaborative object transportation task. We achieve better results than with our previous predictor by reaching success rates in testset of up to 93.7% in predicting the force to be exerted by the human and up to 96.5% in the velocity of the human-robot pair during the next 1 s, and up to 91.0% and 95.0% respectively in real experiments. This new architecture also manages to improve inference times by up to 32.8% with different graphics cards. Finally, an ablation test allows us to detect that one of the input variables used so far, such as the position of the task goal, could be discarded allowing this goal to be chosen dynamically by the human instead of being pre-set.

While robots have advanced in understanding their environments, collaborative tasks demand a deeper comprehension of human intentions to mitigate uncertainty. Anticipatory and proactive behaviours are pivotal in enhancing Human-Robot Interactions (HRI), yet literature often conflates these terms. This study elucidates the distinction between anticipation and proactivity, offering clear definitions and exemplifying their implications through a handover scenario. Through a user study with 24 volunteers performing a total of 72 experiments, we have found that humans are able to distinguish both behaviours and that there is a statistically significant increase in the anthropomorphism of the robot when it behaves proactively. Additionally, both anticipation and proactivity show statistically significant increases in multiple aspects of effective HRI (fluency, comfort, performance, etc.). However, no clear preference for either has been detected.

In this work, we analyse the use of a prediction of the human's force in a Human-Robot collaborative object transportation task at a middle distance. We check that this force prediction can improve multiple parameters associated with effective Human-Robot Interaction (HRI) such as perception of the robot's contribution to the task, comfort or trust in the robot in a physical Human Robot Interaction (pHRI). We present a Deep Learning model that allows to predict the force that a human will exert in the next 1$s$using as inputs the force previously exerted by the human, the robot's velocity and environment information obtained from the robot's LiDAR. Its success rate is up to 92.3% in testset and up to 89.1 % in real experiments. We demonstrate that this force prediction, in addition to being able to be used directly to detect changes in the human's intention, can be processed to obtain an estimate of the human's desired trajectory. We have validated this approach with a user study involving 18 volunteers.

When robots interact with humans, limitations in their internal models arise due to the uncertainty and even randomness of human behavior. This has led to attempts to predict human future actions and infer their intent. However, some authors argue for combining inference engines with communication systems that explicitly elicit human intention. This work builds on our Perception-Intention-Action (PIA) cycle, a framework that considers human intention at the same level as perception of the environment. The PIA cycle is used in a collaborative task to compare the effect on different human-robot interaction aspects of using a force predictor that infers human implicit intention versus a communication system that explicitly elicits human intention. A study with 18 volunteers shows that allowing humans to directly express themselves can achieve the same improvement as an intention predictor.

We present a new social robot named IVO, a robot capable of collaborating with humans and solving different tasks. The robot is intended to cooperate and work with humans in a useful and socially acceptable manner to serve as a research platform for long-term Social Human-Robot Interaction. In this paper, we proceed to describe this new platform, its communication skills and the current capabilities the robot possesses, such as, handing over an object to or from a person or performing guiding tasks with a human through physical contact. We describe the social abilities of the IVO robot, furthermore, we present the experiments performed for each robot's capacity using its current version.

Long-distance human-robot collaborative tasks require robust forms of knowledge-sharing among agents in order to optimize the performance of the task. In this paper, we propose to take advantage of the proliferation of mobile phones to use them as a reliable low-cost communication interface, as opposed to the use of specific gadgets or speech and gesture recognition techniques that are highly prone to failure in the presence of noise or occlusions. Our interface is focused on search tasks, and it allows the user to share with other agents real-time information such as their position, their intention or even what they would like the other agents to do. To test its acceptability, a user study was conducted with 20 volunteers in a human-human scenario. A second round of experiments with other 30 volunteers was conducted to test different ways to encourage user interaction with our interface. Finally, real-life experiments were also conducted with a robot to apply learned knowledge to the desired scenario. We found a statistically significant improvement in the amount of information exchanged between agents.