The framework of dynamic movement primitives allows the generation of discrete and periodic trajectories, which can be modulated in various aspects. We propose and evaluate a novel modulation approach that includes force feedback and thus allows physical interaction with objects and the environment. The proposed approach also enables the coupling of independently executed robotic trajectories, simplifying the execution of bimanual and tightly coupled cooperative tasks. We apply an iterative learning control algorithm to learn a coupling term, which is applied to the original trajectory in a feed-forward fashion. The coupling term modifies the trajectory in accordance to either the desired position or external force. The strengths of the approach are shown in bimanual or two-agent obstacle avoidance tasks, where no higher level cognitive reasoning or planning are required. Results of simulated and real-world experiments on the ARMAR-III humanoid robot in interaction and object lifting tasks, and on two KUKA LWR robots in a bimanual setting are presented.