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Using diffeomorphic matching for robot learning from demonstration


Q. Censier

Master Thesis, FS16

In this Master thesis, I present a new robot Learning from Demonstration (LfD) approach based on a diffeomorphic matching algorithm. From a single demonstrated trajectory, the proposed method extrapolates a velocity vector field that generalizes the demonstration into a skill. Used in a controller, it results in a behavior that can reproduce the demonstration with appropriate responses to perturbations or changes in the initial configuration. The method is easy to implement and relies on very few parameters. It is also efficient and has good mathematical properties (the learned vector fields are guaranteed to be globally asymptotically stable), which makes it a possible alternative to existing LfD techniques. I show experimental results for grasping tasks on two different robot arms: a 6-Degrees of Freedom (DoF) CrustCrawler arm, and a 7-DoF arm of the robot Baxter (Rethink Robotics).

Supervisors: Nicolas Perrin, Stephane Doncieux, Florian Dörfler


Type of Publication:

(12)Diploma/Master Thesis

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2017:IFA_5636
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