In the field of Human-Robot Interaction (HRI), a key challenge lies in enabling humans to comprehend the decisions and behaviours of robots. One promising approach involves leveraging Theory of Mind (ToM) frameworks, wherein a robot estimates the mental model that a user holds about its functioning and compares this with the representation of its internal mental model. This comparison allows the robot to identify potential mismatches and generate communicative actions to bridge such gaps. Effective communication requires the robot to maintain unique mental models for each user and personalise explanations based on past interactions. To address this, we propose an architecture grounded in Large Language Models (LLMs) that operationalises this theoretical framework. We demonstrate the feasibility of this approach through qualitative examples, showcasing responses provided by a robot patrolling a geriatric hospital.

Accurate human motion prediction (HMP) is critical for seamless human-robot collaboration, particularly in handover tasks that require real-time adaptability. Despite the high accuracy of state-of-the-art models, their computational complexity limits practical deployment in real-world robotic applications. In this work, we enhance human motion forecasting for handover tasks by leveraging siMLPe [1], a lightweight yet powerful architecture, and introducing key improvements. Our approach, named IntentMotion incorporates intention-aware conditioning, task-specific loss functions, and a novel intention classifier, significantly improving motion prediction accuracy while maintaining efficiency. Experimental results demonstrate that our method reduces body loss error by over 50%, achieves 200× faster inference, and requires only 3% of the parameters compared to existing state-of-the-art HMP models in robotics. These advancements establish our framework as a highly efficient and scalable solution for real-time human-robot interaction.

In recent years, considerable attention has been given to improving human-robot collaboration. Despite advances in robotic capabilities and interaction techniques, achieving a fair distribution of tasks remains challenging due to the dynamic nature of human preferences and situational constraints. This paper presents a novel negotiation framework that enables robots to effectively communicate with humans to facilitate fair and adaptive task allocation. Our approach leverages automated planning techniques with the Planning Domain Definition Language (PDDL), explicitly encoding tasks, constraints, and preferences from both human and robotic perspectives. Task allocation is optimized based on three key criteria: the robot’s effort, the human’s effort, and overall task success. Additionally, we integrate a Natural Language Processing (NLP) model that interprets human preferences and informs the negotiation process, ensuring that the robot generates task proposals aligned with human input. The negotiation follows an alternating-offer protocol, with the robot employing a sigmoid conceder strategy to iteratively refine task allocation, leading to balanced and mutually acceptable plans. To evaluate our approach, we conduct a comprehensive user study with non-trained volunteers interacting with the robot, assessing the effectiveness, fairness, and adaptability of the proposed system in real-world scenarios.

In the field of Human-Robot Interaction (HRI), a fundamental challenge is to facilitate human understanding of robots. The emerging domain of eXplainable HRI (XHRI) investigates methods to generate explanations and evaluate their impact on human-robot interactions. Previous works have highlighted the need to personalise the level of detail of these explanations to enhance usability and comprehension. Our paper presents a framework designed to update and retrieve user knowledge-memory models, allowing for adapting the explanations’ level of detail while referencing previously acquired concepts. Three architectures based on our proposed framework that use Large Language Models (LLMs) are evaluated in two distinct scenarios: a hospital patrolling robot and a kitchen assistant robot. Experimental results demonstrate that a two-stage architecture, which first generates an explanation and then personalises it, is the framework architecture that effectively reduces the level of detail only when there is related user knowledge.

The pursuit of artificial intelligence has long been associated to the the challenge of effectively measuring intelligence. Even if the Turing Test was introduced as a means of assessing a system’s intelligence, its relevance and application within the field of human-robot interaction remain largely underexplored. This study investigates the perception of intelligence in embodied robots by performing a Turing Test within a robotic platform. A total of 34 participants were tasked with distinguishing between AI- and human-operated robots while engaging in two interactive tasks: an information retrieval and a package handover. These tasks assessed the robot’s perception and navigation abilities under both static and dynamic conditions. Results indicate that participants were unable to reliably differentiate between AI- and human-controlled robots beyond chance levels. Furthermore, analysis of participant responses reveals key factors influencing the perception of artificial versus human intelligence in embodied robotic systems. These findings provide insights into the design of future interactive robots and contribute to the ongoing discourse on intelligence assessment in AI-driven systems.

Many research works currently focus on algorithms designed to generate explanations and then evaluate their effect on user trust and understanding of robots. Even though some projects attempt to design understandable interfaces, they usually serve as extra features for solutions that already exist. In this paper, we suggest a user-centric approach to design explainable robot systems from the very beginning. In particular, we provide a participatory design approach that places emphasis on the cooperative design of an understandable and intuitive interface between the user and the robot system. We suggest turning the attention to the robot’s functionality and autonomous behaviours development after this interface has been established. We exemplify how to apply the proposed framework in a geriatric unit at an intermediate care centre.

As robots move from laboratories and industries to the real world, they must develop new abilities to collaborate with humans in various aspects, including human-robot collaborative navigation (HRCN) tasks. Then, it is required to develop general methodologies to evaluate these robots’ behaviors. These methodologies should incorporate objective and subjective measurements. Objective measurements for evaluating a robot’s behavior while navigating with others can be accomplished using social distances in conjunction with task characteristics, people-robot relationships, and physical space. Additionally, the objective evaluation of the task must consider human behavior, which is influenced by changes and the structure of their environment. Subjective evaluations of robot’s behaviors can be conducted using surveys that address various aspects of robot usability. This includes people’s perceptions of their interaction during their collaborative task with the robot, focusing on aspects such as sociability, comfort, and task-intelligence. Moreover, the communicative interaction between the agents (people and robots) involved in the collaborative task should also be evaluated. Therefore, this paper presents a comprehensive methodology for objectively and subjectively evaluating HRCN tasks.

This article introduces “Baby Robot”, a robot designed to improve infants' and toddlers' motor skills. This robot is a car-like toy that moves autonomously by using reinforcement learning and computer vision. Its behaviour consists of escaping from a target infant that has been previously recognized, or at least detected, without compromising the infant's security by avoiding obstacles. Regarding other robots that share this purpose, there is a variety of commercial toys available on the market; however, no one is betting on an intelligent autonomous movement, since they use to repeat simple, yet repetitive movements. In order to examine how that autonomous movement may improve infants' mobility, two crawling toys-one in representation of “Baby Robot” - were tested in a real environment. These real-life experiments were conducted with a safe and approved surrogate of our proposed robot in a kindergarten, where a group of infants interacted with the toys. Improvements in the efficiency of the play-sassion were detected.

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.

In this work we present a novel attention deep learning model that uses context and human intention for 3D human body motion prediction in handover human-robot tasks. This model uses a multi-head attention architecture which incorporates as inputs the human motion, the robot end effector and the position of the obstacles. The outputs of the model are the predicted motion of the human body and the predicted human intention. We use this model to analyze a handover collaborative task with a robot where the robot is able to predict the future motion of the human and use this information in it’s planner. Several experiments are performed where human volunteers fill a standard poll to rate different features, taking into account when the robot uses the prediction versus when the robot doesn’t use the prediction.

The collaboration between humans and robots in an object search task requires the achievement of shared plans obtained from communicating and negotiating. In this work, we assume that the robot computes, as a first step, a multi-agent plan for both itself and the human. Then, both plans are submitted to human scrutiny, who either agrees or modifies it forcing the robot to adapt its own restrictions or preferences. This process is repeated along the search task as many times as required by the human. Our planner is based on a decentralized variant of Monte Carlo Tree Search (MCTS), with one robot and one human as agents. Moreover, our algorithm allows the robot and the human to optimize their own actions by maintaining a probability distribution over the plans in a joint-action space. The method allows an objective function definition over action sequences, it assumes intermittent communication, it is anytime and suitable for on-line replanning. To test it, we have developed a human-robot communication mobile phone interface. Validation is provided by real-life search experiments of a Parcheesi token in an urban space, including also an acceptability study.

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.

In this paper, we present a new method based on the Aerial Social Force Model (ASFM) to allow human-drone side-by-side social navigation in real environments. To tackle this problem, the present work proposes a new nonlinear-based approach using Neural Networks. To learn and test the rightness of the new approach, we built a new dataset with simulated environments and we recorded motion controls provided by a human expert tele-operating the drone. The recorded data is then used to train a neural network which maps interaction forces to acceleration commands. The system is also reinforced with a human path prediction module to improve the drone's navigation, as well as, a collision detection module to completely avoid possible impacts. Moreover, a performance metric is defined which allows us to numerically evaluate and compare the fulfillment of the different learned policies. The method was validated by a large set of simulations; we also conducted real-life experiments with an autonomous drone to verify the framework described for the navigation process. In addition, a user study has been realized to reveal the social acceptability of the method.

This paper presents a new method to allow robots to accompany a person or a group of people imitating pedestrians behavior. Two-people groups usually walk in a side-by-side formation and three-people groups walk in a V-formation so that they can see each other. For this reason, the proposed method combines a Side-by-side and V-formation pedestrian model with the Anticipative Kinodynamic Planner (AKP). Combining these methods, the robot is able to do an anticipatory accompaniment of groups of humans, as well as to avoid static and dynamic obstacles in advance, while keeping the prescribed formations. The proposed framework allows also a dynamical re-positioning of the robot, if the physical position of the partners change in the group formation. Furthermore, people have a randomness factor that the robot has to manage, for that reason, the system was adapted to deal with changes in people's velocity, orientation and occlusions. Finally, the method has been validated using synthetic experiments and real-life experiments with our Tibi robot. In addition, a user study has been realized to reveal the social acceptability of the method.

This paper presents a new model to make robots capable of approaching and engaging people with a human-like behavior, while they are walking in a side-by-side formation with a person. This method extends our previous work [1], which allows the robot to adapt its navigation behaviour according to the person being accompanied and the dynamic environment. In the current work, the robot is able to predict the best encounter point between the human-robot group and the approached person. Then, in the encounter point the robot modifies its position to achieve an engagement with both people. The encounter point is computed using a gradient descent method that takes into account all people predictions. Moreover, we make use of the Extended Social Force Model (ESFM), and it is modified to include the dynamic goal. The method has been validated over several situations and in real-life experiments, in addition, a user study has been realized to reveal the social acceptability of the robot in this task.

In this paper, we propose a novel Aerial Social Force Model (ASFM) that allows autonomous flying robots to accompany humans in urban environments in a safe and comfortable manner. To date, we are not aware of other state-of-the-art method that accomplish this task. The proposed approach is a 3D version of the Social Force Model (SFM) for the field of aerial robots which includes an interactive human-robot navigation scheme capable of predicting human motions and intentions so as to safely accompany them to their final destination. ASFM also introduces a new metric to fine-tune the parameters of the force model, and to evaluate the performance of the aerial robot companion based on comfort and distance between the robot and humans. The presented approach is extensively validated in diverse simulations and real experiments, and compared against other similar works in the literature. ASFM attains remarkable results and shows that it is a valuable framework for social robotics applications, such as guiding people or human-robot interaction.

We propose an efficient Human Robot Interaction approach to efficiently model the appearance of all relevant objects in robot's environment. Given an input video stream recorded while the robot is navigating, the user just needs to annotate a very small number of frames to build specific classifiers for each of the objects of interest. At the core of the method, there are several random ferns classifiers that share the same features and are updated online. The resulting methodology is fast (runs at 8 fps), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier. We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in outdoor and challenging scenarios containing a multitude of different objects. We show that the human can, with minimal effort, provide the robot with a detailed model of the objects in the scene.

Robots accompanying humans is one of the core capacities every service robot deployed in urban settings should have. We present a novel robot companion approach based on the so-called Social Force Model (SFM). A new model of robot-person interaction is obtained using the SFM which is suited for our robots Tibi and Dabo. Additionally, we propose an interactive scheme for robot's human-awareness navigation using the SFM and prediction information. Moreover, we present a new metric to evaluate the robot companion performance based on vital spaces and comfortableness criteria. Also, a multimodal human feedback is proposed to enhance the behavior of the system. The validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.

During the last decade, there has been a growing interest in making autonomous social robots able to interact with people. However, there are still many open issues regarding the social capabilities that robots should have in order to perform these interactions more naturally. In this paper we present the results of several experiments conducted at the Barcelona Robot Lab in the campus of the “Universitat Politècnica de Catalunya” in which we have analyzed different important aspects of the interaction between a mobile robot and nontrained human volunteers. First, we have proposed different robot behaviors to approach a person and create an engagement with him/her. In order to perform this task we have provided the robot with several perception and action capabilities, such as that of detecting people, planning an approach and verbally communicating its intention to initiate a conversation. Once the initial engagement has been created, we have developed further communication skills in order to let people assist the robot and improve its face recognition system. After this assisted and online learning stage, the robot becomes able to detect people under severe changing conditions, which, in turn enhances the number and the manner that subsequent human-robot interactions are performed.

This article presents a novel approach for optimizing locally the work of cooperative robots and obtaining the minimum displacement of humans in a guiding people mission. Unlike other methods, we consider situations where individuals can move freely and can escape from the formation, moreover they must be regrouped by multiple mobile robots working cooperatively. The problem is addressed by introducing a “Discrete Time Motion” model (DTM) and a new cost function that minimizes the work required by robots for leading and regrouping people. The guiding mission is carried out in urban areas containing multiple obstacles and building constraints. Furthermore, an analysis of forces actuating among robots and humans is presented throughout simulations of different situations of robot and human configurations and behaviors.

This paper describes the validation process of a simulation model that have been used to explore the new possibilities of interaction when humans are guided by teams of robots that work cooperatively in urban areas. The set of experiments, which have been recorded as video sequences, show a group of people being guided by a team of three people (who play the role of the guide robots). The model used in the simulation process is called Discrete Time Motion model (DTM) described in [7], where the environment is modeled using a set of potential fields, and people's motion is represented through tension functions. The video sequences were recorded in an urban space of 10:000 m2denominated Barcelona Robot Lab, where people move in the urban space following diverse trajectories. The motion (pose and velocity) of people and robots extracted from the video sequences were compared against the predictions of the DTM model. Finally, we checked the proper functioning of the model by studying the position error differences of the recorded and simulated sequences.

We present a new model for people guidance in urban settings using several mobile robots, that overcomes the limitations of existing approaches, which are either tailored to tightly bounded environments, or based on unrealistic human behaviors. Although the robots motion is controlled by means of a standard particle filter formulation, the novelty of our approach resides in how the environment and human and robot motions are modeled. In particular we define a ¿Discrete-Time- Motion¿ model, which from one side represents the environment by means of a potential field, that makes it appropriate to deal with open areas, and on the other hand the motion models for people and robots respond to realistic situations, and for instance human behaviors such as ¿leaving the group¿ are considered.