Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion
Teguh Santoso Lembono, Carlos Mastalli, Pierre Fernbach, Nicolas Mansard, Sylvain Calinon
Abstract
In this paper, we propose a framework to build a memory of motion for warm-starting an optimal control solver for the locomotion task of a humanoid robot. We use HPP Loco3D, a versatile locomotion planner, to generate offline a set of dynamically consistent whole-body trajectory to be stored as the memory of motion. The learning problem is formulated as a regression problem to predict a single-step motion given the desired contact locations, which is used as a building block for producing multi-step motions. The predicted motion is then used as a warm-start for the fast optimal control solver Crocoddyl. We have shown that the approach manages to reduce the required number of iterations to reach the convergence from ~9.5 to only ~3.0 iterations for the single-step motion and from ~6.2 to ~4.5 iterations for the multi-step motion, while maintaining the solution's quality.
BibTeX
@inproceedings{icra2020_learninghowtowal,
title = {Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion},
author = {Teguh Santoso Lembono and Carlos Mastalli and Pierre Fernbach and Nicolas Mansard and Sylvain Calinon},
booktitle = {ICRA 2020},
year = {2020}
}