Related papers: Mixed Human-Robot Team Surveillance
Predictive modeling has the potential to enhance human decision-making. However, many predictive models fail in practice due to problematic problem formulation in cases where the prediction target is an abstract concept or construct and…
Autonomous multi-robot optical inspection systems are increasingly applied for obtaining inline measurements in process monitoring and quality control. Numerous methods for path planning and robotic coordination have been developed for…
Integrating robots into teams of humans is anticipated to bring significant capability improvements for tasks such as searching potentially hazardous buildings. Trust between humans and robots is recognized as a key enabler for human-robot…
Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite this, we show in a ubiquitous experimental domain,…
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…
Humanoid robots offer significant advantages for search and rescue tasks, thanks to their capability to traverse rough terrains and perform transportation tasks. In this study, we present a task and motion planning framework for search and…
In collaborative robotic applications, human and robot have to work together during a whole shift for executing a sequence of jobs. The performance of the human robot team can be enhanced by scheduling the right tasks to the human and the…
Multi-robot coordination is crucial for autonomous systems, yet real-world deployments often encounter various failures. These include both temporary and permanent disruptions in sensing and communication, which can significantly degrade…
An open problem in autonomous driving research is modeling human driving behavior, which is needed for the planning component of the autonomy stack, safety validation through traffic simulation, and causal inference for generating…
For effective human-robot collaboration, a robot must align its actions with human goals, even as they change mid-task. Prior approaches often assume fixed goals, reducing goal prediction to a one-time inference. However, in real-world…
In human-robot cooperation, the robot cooperates with humans to accomplish the task together. Existing approaches assume the human has a specific goal during the cooperation, and the robot infers and acts toward it. However, in real-world…
Despite cobots have high potential in bringing several benefits in the manufacturing and logistic processes, but their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to…
We focus on the problem of how we can enable a robot to collaborate seamlessly with a human partner, specifically in scenarios where preexisting data is sparse. Much prior work in human-robot collaboration uses observational models of…
Trust in autonomy is essential for effective human-robot collaboration and user adoption of autonomous systems such as robot assistants. This paper introduces a computational model which integrates trust into robot decision-making.…
Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. Human-collective…
Data-driven algorithms for human-centered autonomy use observed data to compute models of human behavior in order to ensure safety, correctness, and to avoid potential errors that arise at runtime. However, such algorithms often neglect…
Robot-assisted navigation is a perfect example of a class of applications requiring flexible control approaches. When the human is reliable, the robot should concede space to their initiative. When the human makes inappropriate choices the…
Recent works explore collaboration between humans and teams of robots. These approaches make sense if the human is already working with the robot team; but how should robots encourage nearby humans to join their teams in the first place?…
Joint activity describes when more than one agent (human or machine) contributes to the completion of a task or activity. Designing for joint activity focuses on explicitly supporting the interdependencies between agents necessary for…
In this work, we develop a game-theoretic modeling of the interaction between a human operator and an autonomous decision aid when they collaborate in a multi-agent task allocation setting. In this setting, we propose a decision aid that is…