Voice command recognition remains relatively unexplored in robotics, with limited insight into user acceptance and real-world performance. In this work we try to address this by offering multiple voice command recognition models encapsulated in a single publicly available ROS node ready to be used by the robotics practitioner. We tested its actual performance with 10 volunteers of different nationalities whose first spoken language is not English. The obtained accuracy in these tests varies between 93.14% and 95.63% depending on the number of considered commands and model size. Finally, we conducted a user study with 23 new volunteers performing a human-robot collaborative transport task to test whether humans are willing to use this type of system despite having a non-negligible delay and failure rate. In addition to improvements in parameters such as comfort and trust in the robot, 86.9% of the volunteers chose this system over a technically more robust one.

This study proposes to improve the reliability, robustness and human-like nature of Human–Robot Collaboration (HRC). For that, the classical Perception–Action cycle is extended to a Perception–Intention–Action (PIA) cycle, which includes an Intention stage at the same level as the Perception one, being in charge of obtaining both the implicit and the explicit intention of the human, opposing to classical approaches based on inferring everything from perception. This complete cycle is exposed theoretically including its use of the concept of Situation Awareness, which is shown as a key element for the correct understanding of the current situation and future action prediction. This enables the assignment of roles to the agents involved in a collaborative task and the building of collaborative plans. To visualize the cycle, a collaborative transportation task is used as a use-case. A force-based model is designed to combine the robot’s perception of its environment with the force exerted by the human and other factors in an illustrative way. Finally, a total of 58 volunteers participate in two rounds of experiments. In these, it is shown that the human agrees to explicitly state their intention without undue extra effort and that the human understands that this helps to minimize robot errors or misunderstandings. It is also shown that a system that correctly combines inference with explicit elicitation of the human’s intention is the best rated by the human on multiple parameters related to effective Human–Robot Interaction (HRI), such as perceived safety or trust in the robot.

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.

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.

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.

Recent research in Human Robot Collaboration (HRC) has spread and specialised in many sub-fields. Many show considerable advances, but the human–robot collaborative navigation (HRCN) field seems to be stuck focusing on implicit collaboration settings, on hypothetical or simulated task allocation problems, on shared autonomy or on having the human as a manager. This work takes a step forward by presenting an end-to-end system capable of handling real-world human–robot collaborative navigation tasks. This system makes use of the Social Reward Sources model (SRS), a knowledge representation to simultaneously tackle task allocation and path planning, proposes a multi-agent Monte Carlo Tree Search (MCTS) planner for human–robot teams, presents the collaborative search as a testbed for HRCN and studies the usage of smartphones for communication in this setting. The detailed experiments prove the viability of the approach, explore collaboration roles adopted by the human–robot team and test the acceptability and utility of different communication interface designs.

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.

In Human-Robot Collaboration (HRC) tasks, the classical Perception-Action cycle can not fully explain the collaborative behaviour of the human-robot pair until it is extended to Perception-Intention-Action (PIA) cycle, giving to the human's intention a key role at the same level of the robot's perception and not as a subblock of this. Although part of the human's intention can be perceived or inferred by the other agent, this is prone to misunderstandings so the true intention has to be explicitly informed in some cases to fulfill the task. Here, we explore both types of intention and we combine them with the robot's perception through the concept of Situation Awareness (SA). We validate the PIA cycle and its acceptance by the user with a preliminary experiment in an object transportation task showing that its usage can increase trust in the robot.

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.

In this work we argue that in Human-Robot Collaboration (HRC) tasks, the Perception-Action cycle in HRC tasks can not fully explain the collaborative behaviour of the human and robot and it has to be extended to Perception-Intention-Action cycle, where Intention is a key topic. In some cases, agent Intention can be perceived or inferred by the other agent, but in others, it has to be explicitly informed to the other agent to succeed the goal of the HRC task. The Perception-Intention-Action cycle includes three basic functional procedures: Perception-Intention, Situation Awareness and Action. The Perception and the Intention are the input of the Situation Awareness, which evaluates the current situation and projects it, into the future situation. The agents receive this information, plans and agree with the actions to be executed and modify their action roles while perform the HRC task. In this work, we validate the Perception-Intention-Action cycle in a joint object transportation task, modeling the Perception-Intention-Action cycle through a force model which uses real life and social forces. The perceived world is projected into a force world and the human intention (perceived or informed) is also modelled as a force that acts in the HRC task. Finally, we show that the action roles (master-slave, collaborative, neutral or adversary) are intrinsic to any HRC task and they appear in the different steps of a collaborative sequence of actions performed during the task.

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.