Generalized orientation learning in robot task space

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

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

6 Citations (Scopus)


In the context of imitation learning, several approaches have been developed so as to transfer human skills to robots, with demonstrations often represented in Cartesian or joint space. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. However, several crucial issues arising from learning orientations have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this paper, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-point and end-point), where both orientation and angular velocity are addressed. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations. Several examples including comparison with the state-of-the-art dynamic movement primitives are provided to verify the effectiveness of our method.

Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation, ICRA 2019
Number of pages7
ISBN (Electronic)9781538660263
Publication statusPublished - 1 May 2019
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Robotics and Automation - Montreal, Canada
Duration: 20 May 201924 May 2019

Publication series

NameIEEE International Conference on Robotics and Automation
ISSN (Print)2152-4092
ISSN (Electronic)2379-9552


ConferenceIEEE International Conference on Robotics and Automation
Abbreviated titleICRA

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