The ability to infer and mediate intentions has been recognized as a crucial task in recent robotics research, where it is agreed that robots are required to be equipped with intentional mechanisms in order to participate in collaborative tasks with humans.

Reasoning about - or rather, perceiving - intentions enables robots to infer what other agents are doing, to communicate what are their plans, or to take proactive decisions. Intent recognition relates to several system requirements, such as the need of an enhanced collaboration mechanism in human-machine interactions, the need for adversarial technology in competitive scenarios, ambient intelligence, or predictive security systems.

When attempting to describe what an intention is, agreement exists to represent it as a plan together with the goal it attempts to achieve. Being compatible with computer science concepts, this representation enables to handle intentions with methodologies based on planning, such as the Planning Domain Description Language or Hierarchical Task Networks.

In this licentiate we describe how intentions can be processed using classical planning methods, with an eye also on newer technologies such as deep networks. Our goal is to study and define computational models that would allow robotic agents to infer, construct and mediate intentions. Additionally, we explore how intentions in the form of abstract plans can be grounded to sensorial data, and in particular we provide discussion on grounding over speech utterances and affordances, that correspond to the action possibilities offered by an environment.