Adaptive collision-free reaching skill learning from demonstration

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In this paper, we considered the task of the robot learning low-level trajectory task in a novel clustered constraint environment. We propose a novel adaptive trajectory algorithm used to generate the necessary trajectory which satisfies the constraint of avoiding collision with an obstacle. Our approach is based on Gaussian mixture model which decomposes the trajectory into several ellipses since the isoline of a single Gaussian model is also an ellipse. Moreover, we employed the principle of the artificial potential field to modify the direction of the motion in the presence of obstacles. Since our approach is based on the underlying reactive skill dynamics, it does not share the same disadvantages as approaches which assume both the model of the task trajectory and the response from the obstacle should be learned from the demonstrations.
Original languageEnglish
Title of host publicationICRRI 2020: Robotics and Rehabilitation Intelligence
Subtitle of host publicationFirst International Conference, ICRRI 2020, Fushun, China, September 9–11, 2020, Proceedings, Part II
EditorsJianhua Qian, Honghai Liu, Jiangtao Cao, Dalin Zhou
Number of pages14
ISBN (Electronic)978-981-33-4932-2
ISBN (Print)978-981-33-4931-5
Publication statusPublished - 3 Jan 2021
Event1st International Conference on Robotics and Rehabilitation Intelligence - Fushun, China
Duration: 9 Sept 202011 Sept 2020

Publication series

NameCommunications in Computer and Information Science
PublisherSpringer, Singapore
ISSN (Print)1865-0929


Conference1st International Conference on Robotics and Rehabilitation Intelligence
Abbreviated titleICRRI 2020


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