In this article, we investigate the role of causal reasoning in robotics research. Inspired by a
categorization of human causal cognition, we propose a categorization of robot causal …

Exploiting robots for activities in human-shared environments, whether warehouses,
shopping centres or hospitals, calls for such robots to understand the underlying physical …

While abstract knowledge like cause-and-effect relations enables robots to problem-solve in
new environments, acquiring such knowledge remains out of reach for many traditional …

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 …

Sensors are routinely mounted on robots to acquire various forms of measurements in spatio-
temporal fields. Locating features within these fields and reconstruction (mapping) of the …

Deploying robots in human-shared spaces requires understanding interactions among
nearby agents and objects. Modelling cause-and-effect relations through causal inference …

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 this …

Robot control policies for temporally extended and sequenced tasks are often characterized
by discontinuous switches between different local dynamics. These change-points are often …

We present SLOT-V, a novel supervised learning framework that learns observer models
(human preferences) from robot motion trajectories in a legibility context. Legibility measures …

Causality is relatively unexplored in robotics even if it is highly relevant, in several respects.
In this paper, we study how a robot's causal understanding can be improved by allowing the …