Learning from Multiple Demonstrations with Different Modes of Operations

Keywords: Gaussian Mixture Regression, Hidden Markov Models, Learning from Demonstration, trajectory reproduction


In this paper, teaching multiple types of complex trajectories at once to a robot in a robust, easy to train model using Learning from Demonstration is studied where the robot is expected to gain the capacity to differentiate between different types of demonstrated trajectories and be able to reproduce these trajectories correctly. Demonstrated trajectories are used to train a Hidden Markov Model (HMM) and a modified version of Gaussian Mixture Regression (GMR) -which utilizes state transition probabilities between states of the HMM, the most probable state the end effector of the robot belongs to in the current reproduction of the trajectory, and previous points in the current reproduction of the trajectory- is used to estimate the trajectory iteratively. A Proportional Derivative (PD) controller is employed for the reproduction. Starting points that are intended to correspond to different types of trajectories which the robot is expected to differentiate between are tested on numerical and simulation experiments. Multiple numerical experiments and simulation experiments showed that our modified algorithm produced comparable results to previous work, and in certain complex trajectories our algorithm was successful where previous work has failed to produce expected results.


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Author Biography

Emre Ugur, Bogazici University
Emre Uğur is an assistant professor in Department of Computer Engineering Department, BogaziciUniversity, Turkey. After receiving his PhD in Computer Engineering from Middle East, he worked atATR Japan as a researcher (2009-2013), at University of Innsbruck as a senior researcher (2013-2016),and at Osaka University as specially appointed assistant professor (2015, 2016). He participated in several EU funded projects, including Xperience, ROSSI, MACS and Swarm-bots, and is currently PI of IMAGINE project supported by European Commission, Horizon 2020 Programme. He is interested in developmental and cognitive robotics, and intelligent and adaptive manipulation.


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How to Cite
U. Bozdogan and E. Ugur, “Learning from Multiple Demonstrations with Different Modes of Operations”, IJISAE, vol. 8, no. 1, pp. 37-44, Mar. 2020.
Research Article