In this work we propose a decision-making system that efficiently learns behaviors in the form of rules using natural human instructions about cause-effect relations in currently observed situations, avoiding complicated ...

Case-Based Reasoning (CBR) is a learning approach that solves current situations by reusing previous solutions that are stored in a case base. In the CBR cycle the reuse step plays an important role into the problem solving ...

To implement an Industry 4.0 Framework in an ongoing industrial manufacturing process to prepare it for complex Artificial Intelligence use cases is not an easy task. The automotive industry is undergoing a large transformation ...

Movement Primitives (MPs) have been widely used over the last years for learning robot motion tasks with direct Policy Search (PS) reinforcement learning. Among them, Probabilistic Movement Primitives (ProMPs) are a kind ...

Movement Primitives (MPs) have been widely adopted for representing and learning robotic movements using Reinforcement Learning Policy Search. Probabilistic Movement Primitives (ProMPs) are a kind of MP based on a stochastic ...

We present a novel approach for learning a finite mixture model on a Riemannian manifold in which Euclidean metrics are not applicable and one needs to resort to geodesic distances consistent with the manifold geometry. ...

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 ...

Per donar una visió coral sobre la necessitat d’incloure la perspectiva de gènere en l’obertura de dades i en l’ús de la intel·ligència artificial, aquest llibre que teniu a les mans —el setè volum de la col·lecció Govern ...

In the on-line version of the EM algorithm introduced by Sato and Ishii (2000), a time-dependent discount factor is introduced for forgetting the effect of the old posterior values obtained with an earlier, inaccurate ...

Manipulation planning of cluttered objects involves a mixture of symbolic and geometric constraints which makes such planning very time consuming and often unsuitable for real applications. We propose to divide the geometric ...