The current spread of social and assistive robotics applications is increasingly highlighting
the need for robots that can be easily taught and interacted with, even by users with no …

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 …

In this paper, we investigated how shared experience-based counterfactual explanations
affected people's performance and robots' persuasiveness during a decision-making task in …

Most existing 6-DoF robot grasping solutions depend on strong supervision on grasp pose
to ensure satisfactory performance, which could be laborious and impractical when the robot …

We demonstrate TRAinAR, an augmented reality (AR)-based tool that is designed to
improve sim2real reinforcement learning (RL) for robots. TRAinAR aims to enable users to …

Providing explanations of a robot's behavior is a key enabler of trust in robots. Such
explanations should be intuitive to people who are not experts in robotics. Prior research …

Augmented reality (AR) has emerged as a transformative tool in reshaping the field of
human-robot interaction (HRI), especially in tasks requiring seamless manipulation and …

Collaborative robots represent a technological leap forward, and their adoption could benefit
many small and medium-sized enterprises (SMEs). Such robots are cost-effective and allow …

We are witnessing the global spread of artificial intelligence (AI) technology in people's
everyday lives. With the advent of the AI surge, the demand for explainable AI (XAI) …

Human-robot interaction (HRI) attracts significant attention from the public due to the ubiquity
of robots in factories, restaurants, and even at home. However, the engagement of users in …