Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner

Philémon Brakel; Steven Bohez; Leonard Hasenclever; Nicolas Heess; Konstantinos Bousmalis

IROS 2022poster23 citations

Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner

Philémon Brakel, Steven Bohez, Leonard Hasenclever, Nicolas Heess, Konstantinos Bousmalis

Abstract

We propose a simple imitation learning procedure for learning locomotion controllers that can walk over very challenging terrains. We use trajectory optimization (TO) to produce a large dataset of trajectories over procedurally generated terrains and use Reinforcement Learning (RL) to imitate these trajectories. We demonstrate with a realistic model of the ANYmal robot that the learned controllers transfer to unseen terrains and provide an effective initialization for fine-tuning on challenging terrains that require exteroception and precise foot placements. Our setup combines TO and RL in a simple fashion that overcomes the computational limitations and need for a robust tracking controller of the former and the exploration and reward-tuning difficulties of the latter.

BibTeX

@inproceedings{iros2022_learningcoordina,
  title = {Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner},
  author = {Philémon Brakel and Steven Bohez and Leonard Hasenclever and Nicolas Heess and Konstantinos Bousmalis},
  booktitle = {IROS 2022},
  year = {2022}
}