Related papers: Perception-Intention-Action Cycle in Human-Robot C…

Human action recognition has drawn a lot of attention in the recent years due to the research and application significance. Most existing works on action recognition focus on learning effective spatial-temporal features from videos, but…

As agents move into shared workspaces and their execution becomes visible, human-agent collaboration faces a fundamental shift from sequential delegation to concurrent co-creation. This raises a new coordination problem: what interaction…

We present a substantial extension of our Human-Aware Task Planning framework, tailored for scenarios with intermittent shared execution experiences and significant belief divergence between humans and robots, particularly due to the…

We present a framework for learning human user models from joint-action demonstrations that enables the robot to compute a robust policy for a collaborative task with a human. The learning takes place completely automatically, without any…

Shared autonomy enables robots to infer user intent and assist in accomplishing it. But when the user wants to do a new task that the robot does not know about, shared autonomy will hinder their performance by attempting to assist them with…

Collaborative robots are increasingly deployed alongside humans in factories, hospitals, schools, and other domains to enhance teamwork and efficiency. Systems that seamlessly integrate humans and robots into cohesive teams for coordinated…

Robotic systems are more present in our society everyday. In human-robot environments, it is crucial that end-users may correctly understand their robotic team-partners, in order to collaboratively complete a task. To increase action…

We present a reinforcement learning based framework for human-centered collaborative systems. The framework is proactive and balances the benefits of timely actions with the risk of taking improper actions by minimizing the total time spent…

We study the problem of safe and intention-aware robot navigation in dense and interactive crowds. Most previous reinforcement learning (RL) based methods fail to consider different types of interactions among all agents or ignore the…

The field of Human-Robot Collaboration (HRC) has seen a considerable amount of progress in recent years. Thanks in part to advances in control and perception algorithms, robots have started to work in increasingly unstructured environments,…

Ensuring safety in human-robot interaction (HRI) is essential to foster user trust and enable the broader adoption of robotic systems. Traditional safety models primarily rely on sensor-based measures, such as relative distance and…

The overarching goal of this work is to efficiently enable end-users to correctly anticipate a robot's behavior in novel situations. Since a robot's behavior is often a direct result of its underlying objective function, our insight is that…

Robot facial expressions and gaze are important factors for enhancing human-robot interaction (HRI), but their effects on human collaboration and perception are not well understood, for instance, in collaborative game scenarios. In this…

Robots sharing their space with humans need to be proactive in order to be helpful. Proactive robots are able to act on their own initiative in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots…

As human-AI cooperation becomes increasingly prevalent, reliable instruments for assessing the subjective quality of cooperative human-AI interaction are needed. We introduce two theoretically grounded scales: the Perceived Cooperativity…

We propose methods for analysis, design, and evaluation of Meaningful Human Control (MHC) for defense technologies from the perspective of military human-machine teaming (HMT). Our approach is based on three principles. Firstly, MHC should…

Human autonomy and sense of agency are increasingly recognised as critical for user well-being, motivation, and the ethical deployment of robots in human-robot interaction (HRI). Given the rapid development of artificial intelligence, robot…

This paper describes HARMONIC, a cognitive-robotic architecture that integrates the OntoAgent cognitive framework with general-purpose robot control systems applied to human-robot teaming (HRT). HARMONIC incorporates metacognition,…

Collaborative robots became a popular tool for increasing productivity in partly automated manufacturing plants. Intuitive robot teaching methods are required to quickly and flexibly adapt the robot programs to new tasks. Gestures have an…

This report outlines the use of a relational representation in a Multi-Agent domain to model the behaviour of the whole system. A desired property in this systems is the ability of the team members to work together to achieve a common goal…