Human interaction involves very sophisticated non-verbal communication skills like understanding the goals and actions of others and coordinating our own actions accordingly. Neuroscience refers to this mechanism as motor resonance, in the sense that the perception of another person's actions and sensory experiences activates the observer's brain as if (s)he would be performing the same actions and having the same experiences. We analyze and model non-verbal cues (arm movements) exchanged between two humans that interact and execute handover actions. The contributions of this paper are the following: (i) computational models, using recorded motion data, describing the motor behaviour of each actor in action-in-interaction situations, (ii) a computational model that captures the behaviour if the "giver" and "receiver" during an object handover action, by coupling the arm motion of both actors, and (iii) embedded these models in the iCub robot for both action execution and recognition. Our results show that: (i) the robot can interpret the human arm motion and recognize handover actions; and (ii) behave in a "human-like" manner to receive the object of the recognized handover action.
@cite_27: As assistive robots become popular in factories and homes, there is greater need for natural, multi-channel communication during collaborative manipulation tasks. Non-verbal communication such as eye gaze can provide information without overloading more taxing channels like speech. However, certain collaborative tasks may draw attention away from these subtle communication modalities. For instance, robot-to-human handovers are primarily manual tasks, and human attention is therefore drawn to robot hands rather than to robot faces during handovers. In this paper, we show that a simple manipulation of a robot’s handover behavior can significantly increase both awareness of the robot’s eye gaze and compliance with that gaze. When eye gaze communication occurs during the robot’s release of an object, delaying object release until the gaze is finished draws attention back to the robot’s head, which increases conscious perception of the robot’s communication. Furthermore, the handover delay increases peoples’ compliance with the robot’s communication over a non-delayed handover, even when compliance results in counterintuitive behavior.
@cite_23: 
@cite_12: With robotics hardware becoming more and more safe and compliant, robots are not far from entering our homes. The robot, that will share the same environment with humans, will be expected to consider the geometry of the interaction and to perform intelligent space sharing.
One approach proposed for an efficient human-robot collaboration is to predict the human movement during the experiment and optimize the path for the robot to avoid colliding with the human @cite_12 @cite_23 . Although, this may work for some scenarios, it does not solve the core problem of HRI, the mutual understanding of each one's action. For these cases, only the robot is understanding the action of the human. One solution found is to delay the handover task of the robot, since that has proven to improve the human understanding of the robot's intention @cite_27 .