Contact Skill Imitation Learning for RobotIndependent Assembly Programming Robotic automation is a key driver for the advancement of technology. The skills of human workers, however, are difficult to program and seem currently unmatched by technical systems. In this work we present a datadriven approach to extract and learn robotindependent contact skills from human demonstrations in simulation environments, using a Long Short Term Memory (LSTM) network. Our model learns to generate errorcorrecting sequences of forces and torques in task space from objectrelative motion, which industrial robots carry out through a Cartesian force control scheme on the real setup. This scheme uses forward dynamics computation of a virtually conditioned twin of the manipulator to solve the inverse kinematics problem. We evaluate our methods with an assembly experiment, in which our algorithm handles part tilting and jamming in order to succeed. The results show that the skill is robust towards localization uncertainty in task space and across different joint configurations of the robot. With our approach, nonexperts can easily program forcesensitive assembly tasks in a robotindependent way.

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.