MO-BBO: Multi-Objective Bilevel Bayesian Optimization for Robot and Behavior Co-Design

Yeonju Kim, Zherong Pan, Kris Hauser

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Robot design is a time-consuming process involving repeated experiments in a variety of environments to optimize multiple, possibly conflicting performance metrics. Moreover, the optimal robot performance for a given design depends on how the robot adapts its behavior to its environment. We propose a multi-objective Bilevel Bayesian optimization (MO-BBO) technique to automate the process of form-behavior co-design. The approach expands the Pareto front of multiple metrics by simultaneously exploring the robot design and behavior. MO-BBO uses a bilevel optimization of the acquisition function with design and behavior parameters being the high- and low-level decision variables, respectively. In the low-level, we always choose environment-aware behaviors that maximize each metric. We evaluate MO-BBO in applications to grasping gripper design and bimanual arm placement, and show that our method can efficiently focus samples on the Pareto front and generate a diversity of designs.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages14004-14010
Number of pages7
ISBN (Electronic)9781728190778
DOIs
StatePublished - 2021
Event2021 IEEE International Conference on Robotics and Automation, ICRA 2021 - Xi'an, China
Duration: May 30 2021Jun 5 2021

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
Volume2021-May
ISSN (Print)1050-4729

Conference

Conference2021 IEEE International Conference on Robotics and Automation, ICRA 2021
Country/TerritoryChina
CityXi'an
Period5/30/216/5/21

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'MO-BBO: Multi-Objective Bilevel Bayesian Optimization for Robot and Behavior Co-Design'. Together they form a unique fingerprint.

Cite this