IEEE Robotics & Automation Magazine - June 2020 - 22

Using Mixture
Density Networks

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Movement
Primitive Learning
and Generalization

By You Zhou, Jianfeng Gao, and Tamim Asfour

R

epresenting robot skills as movement primitives
(MPs) that can be learned from human demonstration
and adapted to new tasks and situations is a
promising approach toward intuitive robot programming. To allow such adaptation, mapping
between task parameters and MP parameters is needed, and
different approaches have been proposed in the literature to
learn such mapping. In human demonstrations, however,
multiple modes and models exist, and these should be taken
into account when learning these mappings and generalized
MP representations.
Here, a challenging problem is mode or model collapse. To
solve this problem, we propose using a mixture density
Digital Object Identifier 10.1109/MRA.2020.2980591
Date of current version: 6 April 2020

22

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IEEE ROBOTICS & AUTOMATION MAGAZINE

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JUNE 2020

network (MDN) that takes task parameters as input and provides a Gaussian mixture model (GMM) of the MP parameters. To avoid mode and model collapse during MDN
training, we introduce an entropy cost to achieve a more balanced association of demonstrations to GMM components.
Since it is often easier to collect failed examples using an
underfitted MDN model instead of additional human demonstrations, we introduce a failure cost to reduce the occurrence of failures in future executions. We evaluated our
approach in simulation and real robot experiments and
showed that the method outperforms previous approaches.
Challenges of Imitation Learning
Over the past decades, robotics researchers have developed
different approaches that enable robots to learn from humans,
imitate human behavior, and autonomously improve their
1070-9932/20©2020IEEE



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IEEE Robotics & Automation Magazine - June 2020

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - June 2020

Contents
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