As robotic systems become increasingly complex, the need for explainable decision-making becomes critical. Existing explainability approaches in robotics typically either focus on individual modules, which can be difficult to query from the …
Robot learning from humans has been proposed and researched for several decades as a means to enable robots to learn new skills or adapt existing ones to new situations. Recent advances in artificial intelligence, including learning approaches like …
Adaptation to user preferences and the ability to infer and interpret human beliefs and intents, known as the Theory of Mind (ToM), are two critical aspects of effective human-robot collaboration. Despite their importance, very few studies have …
Socially assistive robots represent a promising tool in assistive contexts for improving people's quality of life and well-being through social, emotional, cognitive, and physical support. However, the effectiveness of interactions heavily relies on …
Frailty assessment plays a pivotal role in providing older adults care. However, the current process is time-consuming and only measures patients’ completion time for each test. This paper introduces a set of algorithms to be used in robots to …
As robots become more integrated in human spaces, it is increasingly important for them to explain their decisions. These explanations need to be generated automatically in response to decisions taken in dynamic, unstructured environments. However, …