Learning sparse models for task and motion planning
Author(s)Baraban, Brandon(Brandon J.)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Leslie Pack Kaelbling.
MetadataShow full item record
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.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020Cataloged from the official PDF of thesis.Includes bibliographical references (page 37).
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.