Redefining Data Pairing for Motion Retargeting Leveraging a Human Body Prior

TL;DR

This paper introduces a novel, efficient method for collecting high-quality paired human-robot pose data by reversing the traditional data collection process and filtering extreme poses using a human body prior, enhancing motion retargeting accuracy.

Contribution

It presents a new data collection technique that generates feasible human-robot pose pairs by sampling robot poses and converting them, combined with a filtering method using a human body prior, and a two-stage neural network for motion retargeting.

Findings

Filtering improves retargeting quality

Supervised training with high-quality data outperforms unsupervised methods

The method is adaptable to various humanoid robots

Abstract

We propose MR HuBo(Motion Retargeting leveraging a HUman BOdy prior), a cost-effective and convenient method to collect high-quality upper body paired <robot, human> pose data, which is essential for data-driven motion retargeting methods. Unlike existing approaches which collect <robot, human> pose data by converting human MoCap poses into robot poses, our method goes in reverse. We first sample diverse random robot poses, and then convert them into human poses. However, since random robot poses can result in extreme and infeasible human poses, we propose an additional technique to sort out extreme poses by exploiting a human body prior trained from a large amount of human pose data. Our data collection method can be used for any humanoid robots, if one designs or optimizes the system's hyperparameters which include a size scale factor and the joint angle ranges for sampling. In addition to this data collection method, we also present a two-stage motion retargeting neural network that can be trained via supervised learning on a large amount of paired data. Compared to other learning-based methods trained via unsupervised learning, we found that our deep neural network trained with ample high-quality paired data achieved notable performance. Our experiments also show that our data filtering method yields better retargeting results than training the model with raw and noisy data. Our code and video results are available on https://sites.google.com/view/mr-hubo/