The concept of Theory of Mind (ToM) is central to many social robotic studies. It may be invoked during the design of social robots as a way to improve collaboration or create a form of "social intelligence". It is also considered as an established fact and never put in question. However, many scholars have analysed the concept of ToM from a critical perspective and argued that it is, in fact, a theory with significant shortcomings, that rests on neuronormative and neuroprivileged grounds.

Dynamics in Human-AI interaction should lead to more satisfying and engaging collaboration. Key open questions are how to design such interactions and the role personal goals and expectations play. We developed three AI partners of varying initiative (leader, follower, shifting) in a collaborative game called Geometry Friends. We conducted a within-subjects experiment with 60 participants to assess personal AI partner preference and performance satisfaction as well as perceived warmth and competence of AI partners. Results show that AI partners following human initiative are perceived as warmer and more collaborative. However, some participants preferred AI leaders for their independence and speed, despite being seen as less friendly. This suggests that assigning a leadership role to the AI partner may be suitable for time-sensitive scenarios. We identify design factors for developing collaborative AI agents with varying levels of initiative to create more effective human-AI teams that consider context and individual preference.

In this position paper, we argue for the need for deliberation in communication for artificial agents that perform tasks together with humans. Existing works use a set of terms and concepts with different meanings, resulting in ambiguity which does not allow for a general framework. As an initial step towards such a framework, we propose the notion of deliberative communication, clarify the necessary concepts and terminology, highlight the capabilities required in using deliberation for agents that communicate with human users, and discuss the main challenges.

In many robotic applications, especially those involving humans and the environment, linguistic and visual information must be processed jointly and bound together. Existing works either encode the image or the language into a subsymbolic space, like the CLIP model, or create a symbolic space of extracted information, like the object detection models. In this paper, we propose to describe images by nouns and modifiers and introduce a new embedded binding space where the linguistic and visual cues can effectively be bound. We investigate how state-of-the-art models perform in recognizing nouns and modifiers from images, and propose our method by introducing a dataset and CLIP-like recognition techniques based on transfer learning and metric learning. We show real-world experiments that demonstrate the practical applicability of our approach to robotics applications. Our results indicate that our method can surpass the state-of-the-art in recognizing nouns and modifiers from images. Interestingly, our method exhibits a language characteristic related to context sensitivity.

We consider the problem of communication planning for human-machine cooperation in stochastic and partially observable environments. Partially Observable Markov Decision Processes with Information Rewards (POMDPs-IR) form a powerful framework for information-gathering tasks in such environments. We propose an extension of the POMDP-IR model, called a Communicating POMDP-IR (com-POMDP-IR), that allows an agent to proactively plan its communication actions by using an approximation of the human's beliefs. We experimentally demonstrate the capability of our com-POMDPIR agent to limit its communication to relevant information and its robustness to lost messages.

Event exploration is the process of exploring a topologically known environment to gather information about dynamic events in this environment. Using multi-robot systems for event exploration brings major challenges such as finding and communicating relevant information. This paper presents a solution to these challenges in the form of a distributed decision-theoretic model called MAPING (Multi-Agent Planning for INformation Gathering), in which each agent computes a communication and an exploration strategy by assessing the relevance of an observation for another agent. The agents use an extended belief state that contains not only their own beliefs but also approximations of other agents' beliefs. MAPING includes a forgetting mechanism to ensure that the event-exploration remains open-ended. To overcome the resolution complexity due to the extended belief state we use a method based on the well-known adopted assumption of variables independence. We evaluate our approach on different event exploration problems with varying complexity. The experimental results on simulation show the effectiveness of MAPING, its ability to scale up and its ability to face real-word applications.