This article proposes a method for learning and robotic replication of
dynamic collaborative tasks from offline videos. The objective is to extend the
concept of learning from demonstration (LfD) to dynamic scenarios, benefiting
from widely available or easily producible offline videos. To achieve this
goal, we decode important dynamic information, such as the Configuration
Dependent Stiffness (CDS), which reveals the contribution of arm pose to the
arm endpoint stiffness, from a three-dimensional human skeleton model. Next,
through encoding of the CDS via Gaussian Mixture Model (GMM) and decoding via
Gaussian Mixture Regression (GMR), the robot's Cartesian impedance profile is
estimated and replicated. We demonstrate the proposed method in a collaborative
sawing task with leader-follower structure, considering environmental
constraints and dynamic uncertainties. The experimental setup includes two
Panda robots, which replicate the leader-follower roles and the impedance
profiles extracted from a two-persons sawing video
