Learning Search Strategies from Human Demonstration for Robotic Assembly Tasks
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Journal Title
Journal ISSN
Volume Title
Sähkötekniikan korkeakoulu |
Master's thesis
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Author
Date
2018-08-20
Department
Major/Subject
Space Robotics and Automation
Mcode
ELEC3047
Degree programme
Master in Space Science and Technology
Language
en
Pages
66
Series
Abstract
Learning from Demonstration (LfD) has been used in robotics research for the last decades to solve issues pertaining to conventional programming of robots. This framework enables a robot to learn a task simply from a human demonstration. However, it is unfeasible to teach a robot all possible scenarios, which may lead to e.g. the robot getting stuck. In order to solve this, a search is necessary. However, no current work is able to provide a search approach that is both simple and general. This thesis develops and evaluates a new framework based on LfD that combines both of these aspects. A single demonstration of a human search is made and a model of it is learned. From this model a search trajectory is sampled and optimized. Based on that trajectory, a prediction of the encountered environmental forces is made. An impedance controller with feed-forward of the predicted forces is then used to evaluate the algorithm on a Peg-in-Hole task. The final results show that the framework is able to successfully learn and reproduce a search from just one single human demonstration. Ultimately some suggestions are made for further benchmarks and development.Description
Supervisor
Kyrki, VilleThesis advisor
Suomalainen, MarkkuLundell, Jens
Keywords
learning from demonstration, robotics, robotic assembly, search strategies, learning search, compliant motion
