1 citations · 2 across the 4 of their papers we have counts for
6 papers
ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications
Luca Castri, Gloria Beraldo, Sariah Mghames +2
Deploying robots in human-shared spaces requires understanding interactions among nearby agents and objects. Modelling cause-and-effect relations through causal inference aids in p…
Efficient Causal Discovery for Robotics Applications
Luca Castri, Sariah Mghames, Nicola Bellotto
Using robots for automating tasks in environments shared with humans, such as warehouses, shopping centres, or hospitals, requires these robots to comprehend the fundamental physic…
A Neuro-Symbolic Approach for Enhanced Human Motion Prediction
Sariah Mghames, Luca Castri, Marc Hanheide +1
Reasoning on the context of human beings is crucial for many real-world applications especially for those deploying autonomous systems (e.g. robots). In this paper, we present a ne…
Enhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios
Luca Castri, Sariah Mghames, Marc Hanheide +1
Identifying the main features and learning the causal relationships of a dynamic system from time-series of sensor data are key problems in many real-world robot applications. In t…
Towards Long-term Autonomy: A Perspective from Robot Learning
Zhi Yan, Li Sun, Tomas Krajnik +2
In the future, service robots are expected to be able to operate autonomously for long periods of time without human intervention. Many work striving for this goal have been emergi…
From Continual Learning to Causal Discovery in Robotics
Luca Castri, Sariah Mghames, Nicola Bellotto
Reconstructing accurate causal models of dynamic systems from time-series of sensor data is a key problem in many real-world scenarios. In this paper, we present an overview based…