Encoding Bi-manual Coordination Patterns From Human Demonstrations

Humans perform tasks such as bowl mixing bi-manually, but programming them on a robot can be challenging specially in tasks that require force control or on-line stiffness modulation. In this paper we first propose a user-friendly setup for demonstrating bi-manual tasks, while collecting complementary information on motion and forces sensed on a robotic arm, as well as the human hand configuration and grasp information. Secondly for learning the task we propose a method for extracting task constraints for each arm and coordination patterns between the arms. We use a statistical encoding of the data based on the extracted constraints and reproduce the task using a cartesian impedance controller.