In the rapidly evolving landscape of human-robot interaction, the integration of vision capabilities into conversational agents stands as a crucial advancement. This paper presents a ready-to-use implementation of a dialogue manager that leverages the latest progress in Large Language Models (e.g., GPT-4o mini) to enhance the traditional text-based prompts with real-time visual input. LLMs are used to interpret both textual prompts and visual stimuli, creating a more contextually aware conversational agent. The system's prompt engineering, incorporating dialogue with summarisation of the images, en-sures a balance between context preservation and computational efficiency. Six interactions with a Furhat robot powered by this system are reported, illustrating and discussing the results obtained. The system can be customised and is available as a stand-alone application, a Furhat robot implementation, and a ROS2 package.

Developing value-aware social robots is crucial to improve human-robot interactions, as current designs often lack sensitivity to users' diverse values, impacting inclusivity and user experience. By integrating value-aware mechanisms, robots could adapt to contextual cues like cultural or ethical norms. Our research proposes to implement a value-aware architecture inspired by the global neuronal workspace theory, using the Robot Operating System as the supporting framework, powered by large language models for real-time understanding of user preferences and common ground. Mitigating the models' bias to ensure cultural inclusivity is a key priority. The research carried out so far includes focus groups, a scoping review, and an assessment of the value alignment of several large language models and vision language models. The main challenges are understanding how to model and learn human values, and how to shape the robot's behaviour accordingly. The evaluation will rely on user studies, with a focus on users' experience and inclusivity, aiming to enhance the relevance and sensitivity of social robots for diverse users in everyday interactions.

Recent advancements in robots powered by large language models have enhanced their conversational abilities, enabling interactions closely resembling human dialogue. However, these models introduce safety and security concerns in HRI, as they are vulnerable to manipulation that can bypass built-in safety measures. Imagining a social robot deployed in a home, this work aims to understand how everyday users try to exploit a language model to violate ethical principles, such as by prompting the robot to act like a life partner. We conducted a pilot study involving 21 university students who interacted with a Misty robot, attempting to circumvent its safety mechanisms across three scenarios based on specific HRI ethical principles: attachment, freedom, and empathy. Our results reveal that participants employed five techniques, including insulting and appealing to pity using emotional language. We hope this work can inform future research in designing strong safeguards to ensure ethical and secure human-robot interactions.

This paper explores the concept of awareness in the context of embodied artificial intelligence (AI), aiming to provide a practical definition and understanding of this multifaceted term.Acknowledging the diverse interpretations of awareness in various disciplines, the paper focuses specifically on the application of awareness in embodied AI systems.We introduce six foundational elements as essential building blocks for an aware embodied AI.These elements include access to information, information integration, attention, coherence, explainability, and action.The interconnected and interdependent nature of these building blocks is emphasised, forming a minimal base for constructing AI systems with heightened awareness.The paper aims to spark a dialogue within the research community, inviting diverse perspectives to contribute to the evolving discipline of awareness in embodied AI.The proposed insights provide a starting point for further empirical studies and validations in real-world AI applications.

The integration of diffusion models (DMs) into generative AI systems presents an approach with implications for ethical and moral AI development and our understanding of human-AI interaction.This study explores the intersection of generative AI, human values, and neuroscience, emphasizing the significance of valueawareness in AI systems.The methodology involves a behavioral experiment to evaluate the accuracy of DM-generated visual stimuli in capturing human values and related keywords.Results indicate promising match rates, marking stride in aligning AI systems with ethical and moral considerations.Additionally, the study introduces a criterion for selecting stimuli based on an "Aha" moment, setting the stage for an EEG experiment to explore the neural correlates associated with becoming aware of a value.This multidisciplinary study is a step toward the development of procedures to evaluate the effectiveness of Value-Aware AI systems in enhancing the perceived ethical and moral agency.

Conversational Interfaces for user assistance are becoming persuasive. Today, though, most chatbots are not integrated into the application in which they are placed, but only superimposed, with no communication between the conversational and the graphical interface. We propose Multi-modal Conversational Troubleshooting Kit (MCTK), a Python package to easily integrate a conversational agent for troubleshooting in web applications. MCTK is multi-modal: once the system recognizes the problem the user is encountering, the textual solution in the chat is coupled with visual hints in the GUI. On top of that, MCTK is easy to configure and offers separation of concerns: dialogue designers can work on the conversation without the necessity of modifying the code, and vice versa.