モデル予測制御の模倣と微調整による堅牢で対称的な四足歩行
Imitating and Finetuning Model Predictive Control for Robust and Symmetric Quadrupedal Locomotion
MPCを模倣学習でクローンし、強化学習で微調整するIFMフレームワークを提案し、不整地や滑りやすい地形での四足歩行性能を向上させた。
著者: Donghoon Youm, Hyunyoung Jung, Hyeongjun Kim, Jemin Hwangbo, Hae-Won Park, Sehoon Ha
分類: cs.RO
原文アブストラクト
Control of legged robots is a challenging problem that has been investigated by different approaches, such as model-based control and learning algorithms. This work proposes a novel Imitating and Finetuning Model Predictive Control (IFM) framework to take the strengths of both approaches. Our framework first develops a conventional model predictive controller (MPC) using Differential Dynamic Programming and Raibert heuristic, which serves as an expert policy. Then we train a clone of the MPC using imitation learning to make the controller learnable. Finally, we leverage deep reinforcement learning with limited exploration for further finetuning the policy on more challenging terrains. By conducting comprehensive simulation and hardware experiments, we demonstrate that the proposed IFM framework can significantly improve the performance of the given MPC controller on rough, slippery, and conveyor terrains that require careful coordination of footsteps. We also showcase that IFM can efficiently produce more symmetric, periodic, and energy-efficient gaits compared to Vanilla RL with a minimal burden of reward shaping.
