Learning Cooperative Dynamic Manipulation Skills from Human Demonstration Videos

TL;DR

This paper introduces a method for robots to learn dynamic collaborative manipulation skills from offline human demonstration videos by decoding and reproducing impedance profiles, enabling robots to perform tasks like collaborative sawing.

Contribution

It extends learning from demonstration to dynamic tasks by decoding configuration-dependent stiffness from videos and reproducing impedance profiles using Gaussian mixture models.

Findings

Robots successfully replicated collaborative sawing from human videos.

The method accurately decoded impedance profiles from videos.

Experimental results showed effective dynamic task execution by robots.

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.