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 technique 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.
Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 15, 2017; final manuscript received June 22, 2017; published online November 8, 2017. Assoc. Editor: Prashant Mehta.
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Virani, N., Jha, D. K., Yuan, Z., Shekhawat, I., and Ray, A. (November 8, 2017). "Imitation of Demonstrations Using Bayesian Filtering With Nonparametric Data-Driven Models." ASME. J. Dyn. Sys., Meas., Control. March 2018; 140(3): 030906. https://doi.org/10.1115/1.4037782
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