Gaussian process based model predictive controller for imitation learning

Vladimir Joukov, Dana Kulic

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


Humans still outperform robots in most manipulation and locomotion tasks. Research suggests that humans minimize a task specific cost function when performing movements. In this paper we present a Gaussian Process based method to learn the underlying cost function, without making assumptions on its structure, and reproduce the demonstrated movement on a robot using a linear model predictive control framework. We show that the learned cost function can be used to prioritize between tracking and additional cost functions based on exemplar variance, and satisfy task and joint space constraints. Tuning the weighting between learned position and velocity costs produces trajectories of the desired shape even in the presence of constraints. The approach is validated in simulation with a simple 2dof manipulator showing joint and task space tracking and with a 4dof manipulator reproducing trajectories based on a human handwriting dataset.

Original languageEnglish
Title of host publication2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids 2017)
EditorsDana Kulic, Jun Morimoto, Jan Peters
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781538646786, 9781538646779
ISBN (Print)9781538646793
Publication statusPublished - 22 Dec 2017
Externally publishedYes
EventIEEE-RAS International Conference on Humanoid Robots 2017 - Birmingham, United Kingdom
Duration: 15 Nov 201717 Nov 2017
Conference number: 17th

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580


ConferenceIEEE-RAS International Conference on Humanoid Robots 2017
Abbreviated titleHumanoids 2017
CountryUnited Kingdom

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