Multi-Fidelity Receding Horizon Planning for Multi-Contact Locomotion

Jiayi Wang, Sanghyun Kim, Sethu Vijayakumar, Steve Tonneau

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

Abstract / Description of output

When traversing uneven terrain, humans consider their future steps for choosing the best location and timing of their current step. Likewise, when planning multi-contact motions for legged robots (e.g. humanoids), a ‘prediction horizon’ has to be considered. However, planning several steps ahead increases the dimensionality and non-linearity of an already challenging problem, which makes online planning intractable. We propose to reduce the problem complexity by using convex relaxations in the prediction horizon. We realize this idea within a Receding Horizon Planning (RHP) framework to plan dynamically consistent centroidal trajectories of humanoid walking on uneven terrain. This results in a novel formulation that combines an accurate non-convex model with a relaxed convex model, which we call RHP with multiple levels of model fidelity. We evaluate three candidate multi-fidelity RHPs with convex relaxations of the centroidal dynamics in the prediction horizon. The best candidate is 1.4x-3.0x (average 2.4x) faster than the traditional RHP that employs a single dynamics model over the entire look-ahead horizon. We also validate the resultant centroidal trajectories by tracking them with a whole-body inverse dynamics controller in simulation. Lastly, we find that incorporating angular dynamics in the prediction horizon is important to the success of multi-fidelity RHP.
Original languageEnglish
Title of host publication2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)978-1-7281-9372-4
ISBN (Print)978-1-7281-9373-1
Publication statusPublished - 11 Oct 2021
Event20th IEEE-RAS International Conference on Humanoid Robots - Virtual, Munich, Germany
Duration: 19 Jul 202121 Jul 2021

Publication series

ISSN (Print)2164-0572
ISSN (Electronic)2164-0580


Conference20th IEEE-RAS International Conference on Humanoid Robots
Abbreviated titleHumanoids 202021
Internet address


Dive into the research topics of 'Multi-Fidelity Receding Horizon Planning for Multi-Contact Locomotion'. Together they form a unique fingerprint.

Cite this