A generative human-robot motion retargeting approach using a single depth sensor

Sen Wang, Xinxin Zuo, Runxiao Wang, Fuhua Cheng, Ruigang Yang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

The goal of human-robot motion retargeting is to let a robot follow the movements performed by a human subject. This is traditionally achieved by applying the estimated poses from a human pose tracking system to a robot via explicit joint mapping strategies. In this paper, we present a novel approach that combine the human pose estimation and the motion retarget procedure in a unified generative framework. A 3D parametric human-robot model is proposed that has the specific joint and stability configurations as a robot while its shape resembles a human subject. Using a single depth camera to monitor human pose, we use its raw depth map as input and drive the human-robot model to fit the input 3D point cloud. The calculated joint angles of the fitted model can be applied onto the robots for retargeting. The robot's joint angles, instead of fitted individually, are fitted globally so that the transformed surface shape is as consistent as possible to the input point cloud. The robot configurations including its skeleton proportion, joint limitation, and DoF are enforced implicitly in the formulation. No explicit and pre-defined joints mapping strategies are needed. This framework is tested with both simulations and real robots that have different skeleton proportion and DoFs compared with human to show its effectiveness for motion retargeting.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
Pages5369-5376
Number of pages8
ISBN (Electronic)9781509046331
DOIs
StatePublished - Jul 21 2017
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: May 29 2017Jun 3 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period5/29/176/3/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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