Robots are extending their presence in domestic environments every day, it being more
common to see them carrying out tasks in home scenarios. In the future, robots are expected …

Robotic systems are more present in our society everyday. In human–robot environments, it
is crucial that end-users may correctly understand their robotic team-partners, in order to …

Current Humanoid Service Robot (HSR) behaviours mainly rely on static models that cannot
adapt dynamically to meet individual customer attitudes and preferences. In this work, we …

A long-term goal of reinforcement learning agents is to be able to perform tasks in complex
real-world scenarios. The use of external information is one way of scaling agents to more …

Reinforcement learning refers to a machine learning paradigm in which an agent interacts
with the environment to learn how to perform a task. The characteristics of the environment …

Interactive reinforcement learning proposes the use of externally sourced information in
order to speed up the learning process. When interacting with a learner agent, humans may …

Interactive reinforcement learning has allowed speeding up the learning process in
autonomous agents by including a human trainer providing extra information to the agent in …

Interactive reinforcement learning methods utilise an external information source to evaluate
decisions and accelerate learning. Previous work has shown that human advice could …

Human companion robots need to be sociable and responsive towards emotions to better
interact with the human environment they are expected to operate in. This paper is based on …

Emotions can provide a natural communication modality to complement the existing multi-
modal capabilities of social robots, such as text and speech, in many domains. We …