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REPRODUCING ENCODED TRAJECTORIES FOR NOVEL LOCATIONS

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Abstract

Trajectory learning from demonstrations has the ability to interactively teach robots newskills, eliminating the need for manual programming of a desired behavior. In this thesis, I have implemented Trajectory Learning from Generalized Cylinders (TLGC), a trajectory-based learning approach through demonstrations. Generalized Cylinder (GC) is a geometric representation composed of an arbitrary space curve defined as the spine, along with a varying cross section. GCs are able to extract implicit characteristics of a skill by encoding the demonstration space. They are able to reproduce multiple trajectories maintaining the characteristics of the demonstrated trajectory. This constructed model can be used to generalize a skill for unforeseen circumstances through Laplacian trajectory editing. This is achieved by adding fixed points that must be passed through, while keeping the local shape similar. This work has been validated through a real-world demonstration with a Hello Robot – Stretch RE1. The strengths and weaknesses of this approach are discussed in this thesis.

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42 pages

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Date Issued

2023-08

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Keywords

Generalization; Learning from Demonstrations; Robotics; Trajectory Editing; Trajectory Learning

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Committee Chair

Kress Gazit, Hadas

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Committee Member

Bhattacharjee, Tapomayukh

Degree Discipline

Mechanical Engineering

Degree Name

M.S., Mechanical Engineering

Degree Level

Master of Science

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Government Document

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dissertation or thesis

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