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A Learning from demonstration approach for robot trajectories through motion-sensing human demonstrations

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hdl:2117/192255

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Poc López, Ángel
Tutor / directorAranda López, Joan; Angulo Bahón, CecilioMés informacióMés informacióMés informació
Document typeMaster thesis
Date2020-04-20
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
The objective of this thesis is to teach a Baxter robot to learn certain arm trajectories. The robot must be capable of generalizing the primitive movement of the trajectory to new unseen poses. The thesis is framed within a robotized kitchen project with aims to help people with mobility problems. To solve this problem end, a human will record demonstrations, which will be translated to the robots’ morphology using an Inverse Kinematics (IK) module. For the learning part Dynamic Movement Primitives (DMP) will be used, due to their capability to take profit of human experience. The proposed system works in the majority of the scenarios, but, it would be expected to behave better when generalizing to new orientations of the arm. However a proposal has been suggested to correct this issue.
SubjectsKinect (Programmable controller), Robotics, Computer vision, Kinect (Controlador programable), Robòtica, Visió per ordinador
DegreeMÀSTER UNIVERSITARI EN INTEL·LIGÈNCIA ARTIFICIAL (Pla 2017)
URIhttp://hdl.handle.net/2117/192255
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  • Màsters oficials - Master in Artificial Intelligence - MAI [254]
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