Learning sparse models for task and motion planning
Name
1192538796-MIT.pdf
Size
2.33 MB
Format
Adobe PDF
Checksum (MD5)
b952e6c5c7b77ba35ad631b88f717c28
Author(s)
Baraban, Brandon(Brandon J.)
Advisor(s)
Leslie Pack Kaelbling.
Date Issued
2020
Publisher
Massachusetts Institute of Technology
Abstract
In order for a robot to be able to act effectively in the complex environments we live in, it must be able to have an understanding of how its actions may effect the objects around it. In this work, we present a method for learning the relationships between some robotic action and the objects in the environment in order to predict which objects will be affected by that action. By leveraging the constraint that the effect of the robotic action is sparse, we are able to successfully learn these relationships, and apply them to make learning models of these actions more efficient, and also enable existing models to be more effective when used for task and motion planning.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
Cataloged from the official PDF of thesis.
Includes bibliographical references (page 37).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
Persistent DSpace Link
