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An Integrated Physical-Learning Model of Physical Human-Robot Interactions: A Bikebot Riding Example

[+] Author Affiliations
Kuo Chen, Jingang Yi

Rutgers University, Piscataway, NJ

Yizhai Zhang

Northwestern Polytechnical University, Xi’an, Shaanxi, China

Paper No. DSCC2014-6007, pp. V003T42A002; 10 pages
doi:10.1115/DSCC2014-6007
From:
  • ASME 2014 Dynamic Systems and Control Conference
  • Volume 3: Industrial 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
  • San Antonio, Texas, USA, October 22–24, 2014
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-4620-9
  • Copyright © 2014 by ASME

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 learning-based 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.

Copyright © 2014 by ASME
Topics: Robots

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