Publications by Riccardo Catalini

Humanoid robots are increasingly being integrated into diverse scenarios, such as healthcare facilities, social settings, and workplaces. As the need for intuitive control by non-expert users grows, many studies have explored the use of Artificial Intelligence to enable communication and control. However, these approaches are often tailored to specific robots due to the absence of standardized conventions and notation. This study addresses the challenges posed by these inconsistencies and investigates their impact on the ability of Large Language Models (LLMs) to generate accurate 3D robot poses, even when detailed robot specifications are provided as input.

In this study, we demonstrate the capabilities of state-of-the-art Large Language Models (LLMs) in teaching social robots to perform specific actions within a 3D environment. Specifically, we introduce the use of LLMs to generate sequences of 3D joint angles - in both zero-shot and one-shot prompting - that a humanoid robot must follow to perform a given action. This work is driven by the growing demand for intuitive interactions with social robots: indeed, LLMs could empower non-expert users to operate and benefit from robotic systems effectively. Additionally, this method leverages the possibility to generate synthetic data without effort, enabling privacy-focused use cases. To evaluate the output quality of seven different LLMs, we conducted a blind user study to compare the pose sequences. Participants were shown videos of the well-known NAO robot performing the generated actions and were asked to identify the intended action and choose the best match with the original instruction from a collection of candidates created by different LLMs. The results highlight that the majority of LLMs are indeed capable of planning correct and complete recognizable actions, showing a novel perspective of how AI can be applied to social robotics.