In human–robot collaboration, repeated failures are inevitable and can undermine trust and perceptions of robot intelligence. While some failures severely disrupt tasks and others are relatively benign, their cumulative impact on trust is not clearly understood. We investigated whether users perceive repeated failures of the same type differently from varied failures, and how robot awareness of its own failures affects these perceptions. In a collaborative physical task with 54 participants, we manipulated failure sequence (homogeneous vs. heterogeneous) and awareness (none, partial, full). Results show that trust and perceived intelligence were influenced by both current and prior failures, with homogeneous sequences leading to smaller reductions in these evaluations compared to heterogeneous ones. Robots displaying awareness, whether partial or full, were consistently rated higher than unaware robots, particularly for grasping and planning failures. Our findings provide a deeper understanding of how failure type, sequence, and robot awareness shape users’ perceptions of collaborative robots.

Office Assistant Robots (OARs) offer a promising solution to proactively provide in-situ support to enhance employee well-being and productivity in office spaces. We introduce OfficeMate, a social OAR designed to assist with practical tasks, foster social interaction, and promote health and well-being. Through a pilot evaluation with seven participants in an office environment, we found that users see potential in OARs for reducing stress and promoting healthy habits and value the robot's ability to provide companionship and physical activity reminders in the office space. However, concerns regarding privacy, communication, and the robot's interaction timing were also raised. The feedback highlights the need to carefully consider the robot's appearance and behaviour to ensure it enhances user experience and aligns with office social norms. We believe these insights will better inform the development of adaptive, intelligent OAR systems for future office space integration.

Robots are prone to making errors, which can negatively impact their credibility as teammates during collaborative tasks with human users. Detecting and recovering from these failures is crucial for maintaining effective level of trust from users. However, robots may fail without being aware of it. One way to detect such failures could be by analysing humans' non-verbal behaviours and reactions to failures. This study investigates how human gaze dynamics can signal a robot's failure and examines how different types of failures affect people's perception of robot. We conducted a user study with 27 participants collaborating with a robotic mobile manipulator to solve tangram puzzles. The robot was programmed to experience two types of failures -executional and decisional- occurring either at the beginning or end of the task, with or without acknowledgement of the failure. Our findings reveal that the type and timing of the robot's failure significantly affect participants' gaze behaviour and perception of the robot. Specifically, executional failures led to more gaze shifts and increased focus on the robot, while decisional failures resulted in lower entropy in gaze transitions among areas of interest, particularly when the failure occurred at the end of the task. These results highlight that gaze can serve as a reliable indicator of robot failures and their types, and could also be used to predict the appropriate recovery actions.

Detecting robot failures during collaborative tasks is crucial for maintaining trust in human-robot interactions. This study investigates user gaze behaviour as an indicator of robot failures, utilising machine learning models to distinguish between non-failure and two types of failures: executional and decisional. Eye-tracking data were collected from 26 participants collaborating with a robot on Tangram puzzle-solving tasks. Gaze metrics, such as average gaze shift rates and the probability of gazing at specific areas of interest, were used to train machine learning classifiers, including Random Forest, AdaBoost, XGBoost, SVM, and CatBoost. The results show that Random Forest achieved 90 % accuracy for detecting executional failures and 80 % for decisional failures using the first 5 seconds of failure data. Real-time failure detection was evaluated by segmenting gaze data into intervals of 3, 5, and 10 seconds. These findings highlight the potential of gaze dynamics for real-time error detection in human-robot collaboration.

Human-robot interaction (HRI) is an interdisciplinary field that utilises both quantitative and qualitative methods. While ROSBags, a file format within the Robot Operating System (ROS), offer an efficient means of collecting temporally synched multimodal data in empirical studies with real robots, there is a lack of tools specifically designed to integrate qualitative coding and analysis functions with ROSBags. To address this gap, we developed ROSAnnotator, a web-based application that incorporates a multimodal Large Language Model (LLM) to support both manual and automated annotation of ROSBag data. ROSAnnotator currently facilitates video, audio, and transcription annotations and provides an open interface for custom ROS messages and tools. By using ROSAnnotator, researchers can streamline the qualitative analysis process, create a more cohesive analysis pipeline, and quickly access statistical summaries of annotations, thereby enhancing the overall efficiency of HRI data analysis. https://github.com/CHRI-Lab/ROSAnnotator