most citedEnhancing Causal Discovery from Robot Sensor Data in Dynamic Scenarios

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

cs.RO2024

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…

cs.RO2023

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…

cs.AI2023

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…

cs.RO20231 cited

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…

cs.RO2023

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…