Machine learning for robot motion planning / Clark Zhang.

Format:

Book

Thesis/Dissertation

Author/Creator:

Zhang, Clark, author.

Contributor:

Ribeiro, Alejandro, degree supervisor.

University of Pennsylvania. Department of Electrical and Systems Engineering, degree granting institution.

Language:

English

Subjects (All):

Robotics.

Computer science.

Artificial intelligence.

Electrical and systems engineering--Penn dissertations.

Penn dissertations--Electrical and systems engineering.

Local Subjects:

Robotics.

Computer science.

Artificial intelligence.

Electrical and systems engineering--Penn dissertations.

Penn dissertations--Electrical and systems engineering.

Genre:

Academic theses.

Physical Description:

1 online resource (156 pages)

Contained In:

Dissertations Abstracts International 83-03B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2021.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

Summary:

Robot motion planning is a field that encompasses many different problems and algorithms. From the traditional piano mover's problem to more complicated kinodynamic planning problems, motion planning requires a broad breadth of human expertise and time to design well functioning algorithms. A traditional motion planning pipeline consists of modeling a system and then designing a planner and planning heuristics. Each part of this pipeline can incorporate machine learning. Planners and planning heuristics can benefit from machine learned heuristics, while system modeling can benefit from model learning. Each aspect of the motion planning pipeline comes with trade offs between computational effort and human effort. This work explores algorithms that allow motion planning algorithms and frameworks to find a compromise between the two. First, a framework for learning heuristics for sampling-based planners is presented. The efficacy of the framework depends on human designed features and policy architecture. Next, a framework for learning system models is presented that incorporates human knowledge as constraints. The amount of human effort can be modulated by the quality of the constraints given. Lastly, semi-automatic constraint generation is explored to enable a larger range of trade-offs between human expert constraint generation and data driven constraint generation. We apply these techniques and show results in a variety of robotic systems.

Notes:

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Advisors: Ribeiro, Alejandro; Committee members: Chaudhari, Pratik; Jayaraman, Dinesh; Jakubczak, Szymon.

Department: Electrical and Systems Engineering.

Ph.D. University of Pennsylvania 2021.

Local Notes:

School code: 0175

ISBN:

9798535569383

Access Restriction:

Restricted for use by site license.

This item must not be sold to any third party vendors.