Comprehending User Experience to Improve Human-Robot Interaction

As robotic systems become increasingly integrated into everyday life, improving the user experience in human-robot interaction can facilitate their acceptance and long-term use. This thesis examines how trust and embodiment, two key aspects of the user experience, influence non-expert users in two domains: adaptive collaboration with a robotic arm and embodiment in the robotic limb illusion. These factors are critical for user acceptance, shaping how users interact with robotic systems. The first part investigates trust in adaptive collaborative robots. One approach to enhancing trust is human-centered reinforcement learning, particularly learning from demonstration, where users teach robots new tasks through direct interaction. Two user studies explore how adding human training data to reinforcement learning agents affects trust. The first study examines whether human-trained agents influence user trust, while the second extends this to a physical robotic arm, where participants train the robot themselves. Results show that incorporating expert human data enhances trust, even when performance improvements are not statistically significant. The second part focuses on embodiment in the robotic limb illusion, building on research from the rubber hand illusion. Bayesian causal inference models are applied to analyze embodiment, first by fitting a model to robotic leg illusion data, showing that visual priors play a crucial role in multisensory integration. A follow-up user study gathers sufficient data for individual-level analysis, allowing the model to be adapted for personalized embodiment responses. By estimating user-specific priors, this study provides insights into individual embodiment differences and supports the design of personalized prosthetic and robotic augmentation devices. Together, these studies contribute to human-centered human-robot interaction research by demonstrating how trust and embodiment can be systematically influenced through human data integration and cognitive modeling. The findings suggest that (1) trust in collaborative robots should be considered separately from task performance and (2) personalized embodiment models could enhance prosthetic and robotic limb integration. By addressing these challenges, this thesis advances the development of adaptive, user-friendly robotic systems, ensuring they align more closely with human needs and expectations.