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Conference Papers Year : 2020

Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion

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 $\sim$9.5 to only $\sim$3.0 iterations for the single-step motion and from $\sim$6.2 to $\sim$4.5 iterations for the multi-step motion, while maintaining the solution's quality.

Dates and versions

hal-02973174 , version 1 (20-10-2020)

Identifiers

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Teguh Santoso Lembono, Carlos Mastalli, Pierre Fernbach, Nicolas Mansard, Sylvain Calinon. Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion. IEEE International Conference on Robotics and Automation (ICRA 2020), May 2020, Paris (virtual), France. ⟨10.1109/ICRA40945.2020.9196727⟩. ⟨hal-02973174⟩
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