This paper addresses the problem of having a robot executing motor tasks requested by a human through spoken language. Verbal instructions do not typically have a one-to-one mapping to robot actions, due to various reasons: economy of spoken language, e.g., one short instruction might indeed correspond to a complex sequence of robot actions, and details about action execution might be omitted; grounding, e.g., some actions might need to be added or adapted due to environmental contingencies; embodiment, e.g., a robot might have different means than the human ones to obtain the goals that the instruction refers to. We propose a general cognitive architecture to deal with these issues, based on three steps: i) language-based semantic reasoning on the instruction (high-level), ii) formulation of goals in robot symbols and probabilistic planning to achieve them (mid-level), iii) action execution (low-level). The description of the mid-level is the main focus of this paper. The robot plans are adapted to the current scenario, perceived in real-time and continuously updated, taking in consideration the robot capabilities, modeled through the concept of affordances: this allows for flexibility and creativity in the task execution. We showcase the performance of the proposed architecture with real world experiments using the iCub humanoid robot, also in the presence of unexpected events and action failures.

We propose a mechanism to communicate emotions to humans by using head, torso and arm movements of a humanoid robot, without exploiting its facial features. To this end, we build a library of pre-programmed robot movements and we ask people to attribute emotional scores to these initial movements. The answers are then used to fine-tune motion parameters with an active learning approach.