As robots are increasingly used in human-cluttered environments, the requirement of human-likeness in their movements becomes essential. Although robots perform a wide variety of demanding tasks around the world in factories, remote sites and dangerous environments, they are still lacking the ability to coordinate with humans in simple, every-day life bi-manual tasks, e.g. removing a jar lid. This paper focuses on the introduction of bio-inspired control schemes for robot arms that coordinate with human arms in bi-manual manipulation tasks. Using data captured from human subjects performing a variety of every-day bi-manual life tasks, we propose a bio-inspired controller for a robot arm, that is able to learn human inter- and intra-arm coordination during those tasks. We embed human arm coordination in low-dimension manifolds, and build potential fields that attract the robot to human-like configurations using the probability distributions of the recorded human data. The method is tested using a simulated robot arm that is identical in structure to the human arm. A preliminary evaluation of the approach is also carried out using an anthropomorphic robot arm in bi-manual manipulation task with a human subject.