Identifying Multiple Interaction Events from Tactile Data during RobotHuman Object Transfer During a robot to human object handover task, several intended or unintended events may occur with the object  it may be pulled, pushed, bumped or simply held  by the human receiver. We show that it is possible to differentiate between these events solely via tactile sensors. Training data from tactile sensors were recorded during interaction of human subjects with the object held by a 3finger robotic hand. A Bag of Words approach was used to automatically extract effective features from the tactile data. A Support Vector Machine was used to distinguish between the four events with over 95 percent average accuracy.

Coupling of Arm Movements during HumanRobot Interaction: the handover case Collaboration involves understanding the action of others, as well as acting in a way that can be understood by others. One of those tasks is the handover. In this paper, we study the behaviour of humans during the handover and design the mechanisms allowing a robot to learn from that behaviour.We analyse and model the arm movements of humans while handing over objects to one another. The contributions of this paper are the following: (i) a computational model that captures the behaviour of the xe2x80x9cgiverxe2x80x9d and xe2x80x9creceiverxe2x80x9d of the object, by coupling the arm motion; (ii) discuss this approach amidst a previous coupling strategy; and (iii) embedded the model in the iCub robot for human to robot handovers.Our results show that: (i) the robot can coordinate with the human to timely and safely receive the object; (ii) the robot behaves in a xe2x80x9chumanlikexe2x80x9d manner while receiving the object; and (iii) our approach has significant advantages to the previous approach.