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The rapid advancements in large foundation models and multi-agent systems offer unprecedented capabilities, yet current Human-in-the-Loop (HiTL) paradigms inadequately integrate human expertise, often leading to cognitive overload and…
Translating human intent into robot commands is crucial for the future of service robots in an aging society. Existing Human-Robot Interaction (HRI) systems relying on gestures or verbal commands are impractical for the elderly due to…
The effectiveness of human-robot interaction often hinges on the ability to cultivate engagement - a dynamic process of cognitive involvement that supports meaningful exchanges. Many existing definitions and models of engagement are either…
Despite the recent advancements in robotics and machine learning (ML), the deployment of autonomous robots in our everyday lives is still an open challenge. This is due to multiple reasons among which are their frequent mistakes, such as…
State-of-the-art methods for Human-AI Teaming and Zero-shot Cooperation focus on task completion, i.e., task rewards, as the sole evaluation metric while being agnostic to how the two agents work with each other. Furthermore, subjective…
AI is becoming increasingly integrated into everyday life, both in professional work environments and in leisure and entertainment contexts. This integration requires AI to move beyond acting as an assistant for informational or…
In human-robot interaction (HRI), the beginning of an interaction is often complex. Whether the robot should communicate with the human is dependent on several situational factors (e.g., the current human's activity, urgency of the…
This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI). We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational…
Evaluating and comparing the performance of autonomous Humanoid Robots is challenging, as success rate metrics are difficult to reproduce and fail to capture the complexity of robot movement trajectories, critical in Human-Robot Interaction…
Robot capabilities are maturing across domains, from self-driving cars, to bipeds and drones. As a result, robots will soon no longer be confined to safety-controlled industrial settings; instead, they will directly interact with the…
Large language models (LLMs) are increasingly used as simulated participants in social science experiments, but their behavior is often unstable and highly sensitive to design choices. Prior evaluations frequently conflate base-model…
During human-robot interaction (HRI), we want the robot to understand us, and we want to intuitively understand the robot. In order to communicate with and understand the robot, we can leverage interactions, where the human and robot…
There are a variety of mechanisms (i.e., input types) for real-time human interaction that can facilitate effective human-robot teaming. For example, previous works have shown how teleoperation, corrective, and discrete (i.e., preference…
In the field of Human-Robot Interaction (HRI), a fundamental challenge is to facilitate human understanding of robots. The emerging domain of eXplainable HRI (XHRI) investigates methods to generate explanations and evaluate their impact on…
As the autonomy and capabilities of robotic systems increase, they are expected to play the role of teammates rather than tools and interact with human collaborators in a more realistic manner, creating a more human-like relationship. Given…
As robots get more integrated into human environments, fostering trustworthiness in embodied robotic agents becomes paramount for an effective and safe human-robot interaction (HRI). To achieve that, HRI applications must promote human…
The rise of personal assistant agents, e.g., OpenClaw, highlights the growing potential of large language models to support users across everyday life and work. A core challenge in these settings is proactive assistance, since users often…
To facilitate natural and intuitive interactions with diverse user groups in real-world settings, social robots must be capable of addressing the varying requirements and expectations of these groups while adapting their behavior based on…
Multi-robot systems hold significant promise for social environments such as homes and hospitals, yet existing multi-robot works treat robots as functionally identical, overlooking how robots individual identity shape user perception and…
AI systems increasingly assist human decision making by producing preliminary assessments of complex inputs. However, such AI-generated assessments can often be noisy or systematically biased, raising a central question: how should costly…