People and robots may need to cross each other in narrow spaces when they are sharing environments. It is expected that autonomous robots will behave in these contexts safely but also show social behaviors. Thereby, developing an acceptable behavior for autonomous robots in the area mentioned above is a foreseeable problem for the Human-Robot Interaction (HRI) field. Our current work focuses on integrating legible non-verbal behaviors into the robot's social navigation to make nearby humans aware of its intended trajectory. Results from a within-subjects study involving 33 participants show that deictic gestures as navigational cues for humanoid robots result in fewer navigation conflicts than the use of a simulated gaze. Additionally, an increase in the perceived anthropomorphism is found when the robot uses the deictic gesture as a cue. These findings show the importance of social behaviors for people avoidance and suggest a paradigm of such behaviors in future humanoid robotic applications.

On 31 January 2020, the World Health Organization (WHO) declared a global emergency after the discovery of a new pandemic disease that caused severe lung problems. The spread of the disease at an international level drew the attention of many researchers who attempted to find solutions to ameliorate the problem. The implementation of robotics has been one of the proposed solutions, as automated humanoid robots can be used in many situations and limit the exposure of humans to the disease. Many humanoid robot implementations are found in the literature; however, most of them have some distinct drawbacks, such as a high cost and complexity. Our research proposes a novel, secure and efficient programmable system using a humanoid robot that is able to autonomously move and detect survivors in emergency scenarios, with the potential to communicate verbally with victims. The proposed humanoid robot is powered by the cloud and benefits from the powerful storage, computation, and communication resources of a typical modern data center. In order to evaluate the proposed system, we conducted multiple experiments in synthetic hazardous environments.

When navigating in a shared environment, the extent to which robots are able to effectively use signals for coordinating with human behaviors can ameliorate dissatisfaction and increase acceptance. In this paper, we present an online video study to investigate whether familiar acoustic signals can improve the legibility of a robot’s navigation behavior. We collected the responses of 120 participants to evaluate their perceptions of a robot that communicates with one of the three used non-verbal navigational cues (an acoustic signal, an acoustic in pair with a visual signal, and a dissimilar frequency acoustic signal). Our results showed a significant legibility improvement when the robot used both light and acoustic signals to communicate its intentions compared to using only the same acoustic sound. Additionally, our findings highlighted that people also perceived differently the robot’s intentions when they were expressed through two frequencies of the mere sound. The results of this work suggest a paradigm that can help the development of mobile service robots in public spaces.

Providing a robot with the capabilities of understanding and effectively adapting its behaviour based on human mental states is a critical challenge in Human–Robot Interaction, since it can significantly improve the quality of interaction between humans and robots. In this work, we investigate whether considering human mental states in the decision-making process of a robot improves the transparency of its behaviours and mitigates potential human’s false beliefs about the environment during collaborative scenarios. We used Bayesian inference within a Hierarchical Reinforcement Learning algorithm to include human desires and beliefs into the decision-making processes of the robot, and to monitor the robot’s decisions. This approach, which we refer to as Hierarchical Bayesian Theory of Mind, represents an upgraded version of the initial Bayesian Theory of Mind, a probabilistic model capable of reasoning about a rational agent’s actions. The model enabled us to track the mental states of a human observer, even when the observer held false beliefs, thereby benefiting the collaboration in a multi-goal task and the interaction with the robot. In addition to a qualitative evaluation, we conducted a between-subjects study (110 participants) to evaluate the robot’s perceived Theory of Mind and its effects on transparency and false beliefs in different settings. Results indicate that a robot which considers human desires and beliefs increases its transparency and reduces misunderstandings. These findings show the importance of endowing Theory of Mind capabilities in robots and demonstrate how these skills can enhance their behaviours, particularly in human–robot collaboration, paving the way for more effective robotic applications.

The lack of transparency in robotic learning processes poses a significant challenge to effective human-robot collaboration. This is particularly relevant in non-industrial settings because it prevents humans from adequately comprehending a robot’s intentions, progress, and decision-making rationale, which is essential for seamless interaction. To address this issue, this work presents a study where users observe a robot endowed with three distinct emotional/behavioural mechanisms for conveying transparent information about its learning process. The proposed mechanisms use inner speech, emotions, and a combination of the two communication styles (hybrid). To assess and evaluate the transparency of these behavioural models, a between-subject study was conducted with 108 participants. Results indicate that the people’s perception of the robot’s warmth dimension increased when it utilized a hybrid model to explain its learning state. Additionally, increased transparency was observed when the robot used inner speech during the learning process.