Robust Predictive Control for Quadrupedal Locomotion: Learning to Close the Gap Between Reduced- and Full-Order Models

Simple item page
dc.contributor.authorPandala, Abhisheken
dc.contributor.authorFawcett, Randall T.en
dc.contributor.authorRosolia, Ugoen
dc.contributor.authorAmes, Aaron D.en
dc.contributor.authorHamed, Kaveh Akbarien
dc.date.accessioned2023-01-31T17:53:32Zen
dc.date.available2023-01-31T17:53:32Zen
dc.date.issued2022-07-01en
dc.date.updated2023-01-30T17:14:40Zen
dc.description.abstractTemplate-based reduced-order models have provided a popular methodology for real-time trajectory planning of dynamic quadrupedal locomotion. However, the abstraction and unmodeled dynamics in template models significantly increase the gap between reduced- and full-order models. This letter presents a computationally tractable robust model predictive control (RMPC) formulation, based on convex quadratic programs (QP), to bridge this gap. The RMPC framework considers the single rigid body model subject to a set of unmodeled dynamics and plans for the optimal reduced-order trajectory and ground reaction forces (GRFs). The generated optimal GRFs of the high-level RMPC are then mapped to the full-order model using a low-level nonlinear controller based on virtual constraints and QP. The proposed hierarchical control framework is employed for locomotion over rough terrains. We leverage deep reinforcement learning to train a neural network to compute the set of unmodeled dynamics for the RMPC framework. The proposed controller is finally validated via extensive numerical simulations and experiments for robust and blind locomotion of the A1 quadrupedal robot on different terrains.en
dc.description.versionAccepted versionen
dc.format.extentPages 6622-6629en
dc.format.extent8 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/LRA.2022.3176105en
dc.identifier.eissn2377-3766en
dc.identifier.issn2377-3766en
dc.identifier.issue3en
dc.identifier.orcidAkbari Hamed, Kaveh [0000-0001-9597-1691]en
dc.identifier.urihttp://hdl.handle.net/10919/113588en
dc.identifier.volume7en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000805161600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRoboticsen
dc.subjectLegged robotsen
dc.subjectmotion controlen
dc.subjectmulti-contact whole-body motion planning and controlen
dc.subjectDYNAMICSen
dc.subjectSYSTEMSen
dc.subjectWALKINGen
dc.subjectGAITen
dc.subjectBioengineeringen
dc.titleRobust Predictive Control for Quadrupedal Locomotion: Learning to Close the Gap Between Reduced- and Full-Order Modelsen
dc.title.serialIEEE Robotics and Automation Lettersen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Engineeringen
pubs.organisational-group/Virginia Tech/Engineering/Mechanical Engineeringen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Engineering/COE T&R Facultyen

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
final_paper.pdf
Size:
1.59 MB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download

Collections

All Faculty Deposits
Scholarly Works, Mechanical Engineering