Learning cooperative dynamic manipulation skills from human demonstration videos

Abstract

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.