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Imitation of Demonstrations using Bayesian Filtering with Nonparametric Data-Driven Models

[+] Author and Article Information
Nurali Virani

Department of Mechanical and Nuclear Engineering; GE Global Research, Niskayuna, NY 12309
nurali.virani88@gmail.com

Devesh K. Jha

Department of Mechanical and Nuclear Engineering; Mitsubishi Electric Research Laboratories, Cambridge, MA 02139
devesh.dkj@gmail.com

Zhenyuan Yuan

Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802
zqy5086@psu.edu

Ishana Shekhawat

Department of Mechanical and Nuclear Engineering
ibs5048@psu.edu

Asok Ray

Department of Mechanical and Nuclear Engineering; Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802
axr2@psu.edu

1Corresponding author.

ASME doi:10.1115/1.4037782 History: Received February 15, 2017; Revised June 22, 2017

Abstract

This paper addresses the problem of learning dynamic models of hybrid systems from demonstrations and then the problem of imitation of those demonstrations by using Bayesian filtering. A linear programming-based approach is used to develop nonparametric kernel-based conditional density estimation to infer accurate and concise dynamic models of system evolution from data. The training data for these models have been acquired from demonstrations by teleoperation. The trained data-driven models for mode-dependent state evolution and state-dependent mode evolution are then used online for imitation of demonstrated tasks via particle filtering. The results of simulation and experimental validation with a hexapod robot are reported to establish generalization of the proposed learning and control algorithms.

Copyright (c) 2017 by ASME
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