In this article, we aim to evaluate the role of robots’ personality-driven behavioural patterns on users’ intention to use in an entertainment scenario. Toward such a goal, we designed two personalities: one introverted ...

Programming by demonstration techniques facilitate the programming of robots. Some of them allow the generalization of tasks through parameters, although they require new training when trajectories different from the ones ...

Human environments are challenging for robots, which need to be trainable by lay people and learn new behaviours rapidly without disrupting much the ongoing activity. A system that integrates AI techniques for planning and ...

In recent years the topic of combining motion and symbolic planning to perform complex tasks in the field of robotics has received a lot of attention. The underlying idea is to have access at once to the reasoning capabilities ...

For a safe and successful daily living assistance, far from the highly controlled environment of a factory, robots should be able to adapt to ever-changing situations. Programming such a robot is a tedious process that ...

The kinematics of a robot with many degrees of freedom is a very complex function. Learning this function for a large workspace with a good precision requires a huge number of training samples, i.e., robot movements. In ...

Many techniques to reduce the cost at test time in large-scale problems involve a hierarchical organization of classifiers, but are either too expensive to learn or degrade the classification performance. Conversely, in ...

Research in learning from demonstration has focused on transferring movements from humans to robots. However, a need is arising for robots that do not just replicate the task on their own, but that also interact with humans ...

We propose a technique to speed up the learning of the inverse kinematics of a robot manipulator by decomposing it into two or more virtual robot arms. Unlike previous decomposition approaches, this one does not place any ...

Probabilistic planners are very flexible tools that provide good solutions for difficult tasks. However, they rely on a model of the domain and actions, which they have difficulties to learn for complex tasks. We propose ...