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.

For social robots to collaborate effectively, they must infer and correct false human beliefs, especially when misconceptions directly impact task outcomes or pose safety risks to humans. In this work, we investigated whether a robot’s ability to detect and rectify users’ false beliefs improves trust and perceived social competence. In an in-person between-subject study, 98 participants collaborated with a robot to solve a task. Participants interacted with either a robot that actively corrected their false beliefs by using Theory of Mind or one that complied with their incorrect instructions. Contrary to expectations, trust, mental state attribution, and perceived warmth or competence did not differ between groups. The results also showed that human reluctance to trust the robot’s input persisted, suggesting that belief correction alone cannot overcome relational barriers. In addition, the study showed that participants who trusted the robot’s corrections perceived it as more socially attuned.

Social robots hold great promise for supporting children’s learning, yet their effectiveness may depend on how well they align with individual learner characteristics. This study investigates the role of gender in shaping the outcomes of Learning-by-Teaching (LbT) with a social robot. In a primary school setting, 53 children (aged 8–9) participated in French language tasks under either a robot-assisted LbT condition or a self-practice condition. While no significant effects were found, girls consistently showed higher learning and retention gains when teaching the robot compared to practicing alone, an effect not observed in boys. Contrary to expectations, girls and boys spent similar time on task, used help equally, and reported comparable perceptions of the learning activity. Exploratory analyses revealed that girls who found the task more difficult learnt more, aligning with theories of desirable difficulty, and that higher learning gains were linked to lower perceived competence, suggesting possible signs of Imposter Syndrome. These findings highlight the complex interplay between cognitive and emotional factors in robot-assisted learning and emphasise the need for personalised educational technologies that adapt not only to performance but also to learner identity and psychological experience.

Large Language Models (LLMs) are increasingly influential in advancing robotics. This paper introduces ECLAIR (Evaluative Corrective Guidance Language as Reinforcement), a novel framework that leverages LLMs to interpret and incorporate diverse natural language feedback into robotic learning. ECLAIR unifies various forms of human advice into actionable insights within a Reinforcement Learning context, enabling more efficient robot instruction. Experiments with real-world users demonstrate that ECLAIR accelerates the robot’s learning process, aligning its policy closer to optimal from the outset and reducing the need for extensive human intervention. Additionally, ECLAIR effectively integrates multiple types of advice and adapts well to prompt modifications. It also supports multilingual instruction, broadening its applicability and fostering more inclusive human-robot interactions. Project website: https://sites.google.com/view/eclairiros

In order to achieve a widespread adoption of social robots in the near future, we need to design intelligent systems that are able to autonomously understand our beliefs and preferences. This will pave the foundation for a new generation of robots able to navigate the complexities of human societies. To reach this goal, we look into Theory of Mind (ToM): the cognitive ability to understand other agents’ mental states. In this paper, we rely on a probabilistic ToM model to detect when a human has false beliefs with the purpose of driving the decision-making process of a collaborative robot. In particular, we recreate an established psychology experiment involving the search for a toy that can be secretly displaced by a malicious individual. The results that we have obtained in simulated experiments show that the agent is able to predict human mental states and detect when false beliefs have arisen. We then explored the set-up in a real-world human interaction to assess the feasibility of such an experiment with a humanoid social robot.

Social interaction is the new frontier in contemporary robotics: we want to build robots that blend with ease into our daily social environments, following their norms and rules. The cognitive skill that bootstraps social awareness in humans is known as "intention reading" and it allows us to interpret other agents' actions and assign them meaning. Given its centrality for humans, it is likely that intention reading will foster the development of robotic social understanding. In this paper, we present an artificial cognitive architecture for intention reading in human-robot interaction (HRI) that makes use of social cues to disambiguate goals. This is accomplished by performing a low-level action encoding paired with a high-level probabilistic goal inference. We introduce a new clustering algorithm that has been developed to differentiate multi-sensory human social cues by performing several levels of clustering on different feature-spaces, paired with a Bayesian network that infers the underlying intention. The model has been validated through an interactive HRI experiment involving a joint manipulation game performed by a human and a robotic arm in a toy block scenario. The results show that the artificial agent was capable of reading the intention of its partner and cooperate in mutual interaction, thus validating the novel methodology and the use of social cues to disambiguate goals, other than demonstrating the advantages of intention reading in social HRI.