An integrated physical-learning model of physical human-robot interactions: A bikebot riding example

Kuo Chen, Yizhai Zhang, Jingang Yi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

One of the main challenges for modeling human-robot interactions (pHRI) is the high dimensionality and complexity of human motion. We present an integrated physical-learning modeling framework for pHRI with applications on the bikebot riding example. The modeling framework contains a machine learningbased model of high-dimensional limb motion coupled with a physical principle-based dynamic model for the human trunk and the interacted robot. A new axial linear embedding (ALE) algorithm is introduced to obtain the lower-dimensional latent dynamics for redundant human limb motion. The integrated physical-learning model is used to estimate the human motion through an extended Kalman filter design. No sensors are required and attached on human subject's limb segments. Extensive bikebot riding experiments are conducted to validate the integrated human motion model. Comparison results with other machine learning-based models are also presented to demonstrate the superior performance of the proposed modeling framework.

Original languageEnglish (US)
Title of host publicationIndustrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791846209
DOIs
StatePublished - 2014
EventASME 2014 Dynamic Systems and Control Conference, DSCC 2014 - San Antonio, United States
Duration: Oct 22 2014Oct 24 2014

Publication series

NameASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Volume3

Other

OtherASME 2014 Dynamic Systems and Control Conference, DSCC 2014
Country/TerritoryUnited States
CitySan Antonio
Period10/22/1410/24/14

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'An integrated physical-learning model of physical human-robot interactions: A bikebot riding example'. Together they form a unique fingerprint.

Cite this