University of Illinois Urbana-Champaign

Multi-Objective Bilevel Bayesian optimization for robot and behavior co-design

Kim, Yeonju

Content Files
KIM-THESIS-2021.pdf

Permalink

https://hdl.handle.net/2142/112968

Description

Title
Multi-Objective Bilevel Bayesian optimization for robot and behavior co-design
Author(s)
Kim, Yeonju
Issue Date
2021-06-25
Director of Research (if dissertation) or Advisor (if thesis)
  • Hauser, Kris
  • Ramos, João
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2022-01-12T21:45:20Z
Keyword(s)
  • Robot Design Optimization
  • Robot Arm Placement
  • Gripper Design
Abstract
Traditionally, the robot design process is based on the trial-and-error approach that involves repeated cycles of design, prototyping, and evaluation. During the process, the robot designer should tackle multiple objectives, which are commonly in conflicting relationships. Furthermore, the robot design assessment involves costly behavior optimization and performance evaluation in multiple environments. We propose a Multi-Objective Bilevel Bayesian optimization (MO-BBO) algorithm to automate the co-design process of the robot design and behavior simultaneously. Since the behavior should be optimized for each design and environment, we select the next design candidate in a bilevel manner. Design parameters and behavior parameters are the high- and low-level decision variables, respectively. We applied our algorithm to two robot co-design problems: gripper design problem and robot arm placement problem. MO-BBO was able to effectively expand the Pareto front in objective space on two problems. We extend the robot arm placement problem by integrating human-likeness into objectives. To account for human likeness, we construct trajectory-based metrics to evaluate how well the robot arm follows the human motion trajectories extracted from the TUM Kitchen dataset and how similar the robot arm structure is to the human arm structure while following the trajectories. Compared to the designs generated by using reachability indices, the designs generated by the trajectory-based metrics have better performance when following human motion trajectories, especially in terms of collision rate and structural similarity.
Graduation Semester
2021-08
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/112968
Copyright and License Information
Copyright 2021 Yeonju Kim

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