Human Prediction for the Natural Instruction of Handovers in Human Robot Collaboration Human robot collaboration is aspiring to establish hybrid work environments in accordance with specific strengths of humans and robots. We present an approach of flexibly integrating robotic handover assistance into collaborative assembly tasks through the use of natural communication. For flexibly instructed handovers, we implement recent Convolutional Neural Networks in terms of object detection and grasping of arbitrary objects based on an RGBD camera equipped to a robot following the eyeinhand principle. In order to increase fluency and efficiency of the overall assembly process, we investigate the human ability to instruct the robot predictively with voice commands. We conduct a user study quantitatively and qualitatively evaluating the predictive instruction in order to achieve justintime handovers of tools needed for following subtasks. We compare our predictive strategy with a pure manual assembly having all tools in direct reach and a stepbystep reactive handover. The results reveal that the human is able to predict the handover comparable to algorithmbased predictors. Nevertheless, human prediction does not rely on extensive prior knowledge and is thus suitable for more flexible usage. However, the cognitive workload for the worker is increased compared to manual or reactive assembly.

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.