International audience

International audience

Personalization in social robotics is critical for fostering effective human-robot interactions, yet systems often face the cold start problem, where initial user preferences or characteristics are unavailable. This paper proposes a novel framework called USER-LLM R1 for a user-aware conversational agent that addresses this challenge through dynamic user profiling and model initiation. Our approach integrates chain-of-thought (CoT) reasoning models to iteratively infer user preferences and vision-language models (VLMs) to initialize user profiles from multimodal inputs, enabling personalized interactions from the first encounter. Leveraging a Retrieval-Augmented Generation (RAG) architecture, the system dynamically refines user representations within an inherent CoT process, ensuring contextually relevant and adaptive responses. Evaluations on the ElderlyTech-Vqa Bench demonstrate significant improvements in ROUGE-1 (+23.2%) ROUGE-2 (+0.6%) and ROUGE-L (+8%) F1 scores over state-of-the-art baselines, with ablation studies underscoring the impact of reasoning model size on performance. Human evaluations further validate the framework’s efficacy, particularly for elderly users, where tailored responses enhance engagement and trust. Ethical considerations, including privacy preservation and bias mitigation, are rigorously discussed and addressed to ensure responsible deployment.

hyperref With the increasing presence of social robots in various environments and applications, there is an increasing need for these robots to exhibit socially-compliant behaviors. Legible motion, characterized by the ability of a robot to clearly and quickly convey intentions and goals to the individuals in its vicinity, through its motion, holds significant importance in this context. This will improve the overall user experience and acceptance of robots in human environments. In this paper, we introduce a novel approach to incorporate legibility into local motion planning for mobile robots. This can enable robots to generate legible motions in real-time and dynamic environments. To demonstrate the effectiveness of our proposed methodology, we also provide a robotic stack designed for deploying legibility-aware motion planning in a social robot, by integrating perception and localization components.The code and the data used in this work are available at https://legibot.github.io.

Perceiving the environment from another person’s perspective, in other words, being in someone else’s shoes spatially, is not always an easy task. Perspective-taking can be even more challenging when working with a robot as a collaborator. The study reported here aims at investigating humans’ level 2 spatial perspective-taking performance when interacting with a collaborative robotic arm through a novel inperson experiment. First, a robotic arm drew ambiguous shapes on a whiteboard and participants had to answer questions that require performing spatial perspective-taking. A metric was used to compute a score based on their responses. Second, participants completed the PTSOT, a test measuring spatial orientation and perspective-taking ability. The results revealed a correlation between the scores computed using our metric and those obtained in the PTSOT. This suggests the efficiency of our new setup and associated evaluation metric in assessing spatial perspective-taking skills in a human-robot interaction context, as well as the validity of our findings, in line with prior studies on perspective-taking