The ability to detect and analyze failed executions automatically is crucial for an explainable
and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated …

Robots working in human environments need to adapt to unexpected changes to avoid
failures. This is an open and complex challenge that requires robots to timely predict and …

As robots tackle increasingly complex tasks, the need for explanations becomes essential
for gaining trust and acceptance. Explainable robotic systems should not only elucidate …

The last decades have seen a revolution in autonomous robotics. Deep learning
approaches and their hardware implementations have made it possible to endow robots …

The explainability of a robot's actions is crucial to its acceptance in social spaces. Explaining
why a robot fails to complete a given task is particularly important for non-expert users to be …

Robots operating in real-world environments must reason about possible outcomes of
stochastic actions and make decisions based on partial observations of the true world state …

DH Despite significant improvements in robot capabilities, they are likely to fail in human-
robot collaborative tasks due to high unpredictability in human environments and varying …

We propose SCALE, an approach for discovering and learning a di-verse set of interpretable
robot skills from a limited dataset. Rather than learning a single skill which may fail to …

An autonomous service robot should be able to interact with its environment safely and
robustly without requiring human assistance. Unstructured environments are challenging for …

Current robot autonomy struggles to operate beyond the assumed Operational Design
Domain (ODD), the specific set of conditions and environments in which the system is …