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

Kim, Yeonju

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

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

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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