Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives

João Silvério, Yanlong Huang, Fares J. Abu-Dakka, Leonel Rozo, Darwin G. Caldwell

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

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During the past few years, probabilistic approaches to imitation learning have earned a relevant place in the robotics literature. One of their most prominent features is that, in addition to extracting a mean trajectory from task demonstrations, they provide a variance estimation. The intuitive meaning of this variance, however, changes across different techniques, indicating either variability or uncertainty. In this paper we leverage kernelized movement primitives (KMP) to provide a new perspective on imitation learning by predicting variability, correlations and uncertainty using a single model. This rich set of information is used in combination with the fusion of optimal controllers to learn robot actions from data, with two main advantages: i) robots become safe when uncertain about their actions and ii) they are able to leverage partial demonstrations, given as elementary sub-tasks, to optimally perform a higher level, more complex task. We showcase our approach in a painting task, where a human user and a KUKA robot collaborate to paint a wooden board. The task is divided into two sub-tasks and we show that the robot becomes compliant (hence safe) outside the training regions and executes the two sub-tasks with optimal gains otherwise.
Original languageEnglish
Title of host publicationProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Number of pages8
ISBN (Electronic)978-1-7281-4004-9
ISBN (Print)978-1-7281-4005-6
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventIEEE/RSJ International Conference on Intelligent Robots and Systems - The Venetian Macao, Macau, China
Duration: 4 Nov 20198 Nov 2019

Publication series

NameProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866


ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
Abbreviated titleIROS
Internet address

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