Contact-Implicit Trajectory Optimization With Learned Deformable Contacts Using Bilevel Optimization

Yifan Zhu, Zherong Pan, Kris Hauser

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

Abstract

We present a bilevel, contact-implicit trajectory optimization (TO) formulation that searches for robot trajectories with learned soft contact models. On the lower-level, contact forces are solved via a quadratic program (QP) with the maximum dissipation principle (MDP), based on which the dynamics constraints are formulated in the upper-level TO problem that uses direct transcription. Our method uses a contact model for granular media that is learned from physical experiments, but is general to any contact model that is stick-slip, convex, and smooth. We employ a primal interior-point method with a pre-specified duality gap to solve the lower-level problem, which provides robust gradient information to the upper-level problem. We evaluate our method by optimizing locomotion trajectories of a quadruped robot on various granular terrains offline, and show that we can obtain long-horizon walking gaits of high qualities.

Original languageEnglish (US)
Title of host publication2021 IEEE International Conference on Robotics and Automation, ICRA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9921-9927
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

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