Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …

Artificial intelligence advances have led to robots endowed with increasingly sophisticated
social abilities. These machines speak to our innate desire to perceive social cues in the …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions; especially considering the emergent field of deep RL (DRL). However a …

Recent technological developments in robotics has driven the design and production of
different humanoid robots. Several studies have highlighted that the presence of human-like …

Continual learning is essential for all real-world applications, as frozen pre-trained models
cannot effectively deal with non-stationary data distributions. The purpose of this study is to …

Studying trust in the context of human–robot interaction is of great importance given the
increasing relevance and presence of robotic agents in the social sphere, including …

Amidst the fourth industrial revolution, social robots are resolutely moving from fiction to
reality. With sophisticated artificial agents becoming ever more ubiquitous in daily life …

The reinforcement learning (RL) research area is very active, with an important number of
new contributions, especially considering the emergent field of deep RL (DRL). However, a …

During our everyday lives, we are confronted with a vast amount of information from several
sensory modalities. This multisensory information needs to be appropriately integrated for us …

Intrinsically motivated goal exploration processes enable agents to explore efficiently
complex environments with high-dimensional continuous actions. They have been applied …