# Learning Cooperative Dynamic Manipulation Skills from Human Demonstration Videos

arXiv: https://arxiv.org/abs/2204.04003 · HTML: https://mlsanity.com/paper/2204.04003/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2022.

## Built on by (most cited first, 2 of 2)

- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [Intelligent Framework for Human-Robot Collaboration: Dynamic Ergonomics and Adaptive Decision-Making](https://mlsanity.com/paper/2503.07901/) (2025)

## Abstract (arXiv)

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.