MPCを教師とする四足歩行ロボットのマルチ歩容制御の模倣学習
Imitation Learning from MPC for Quadrupedal Multi-Gait Control
MPC-Netを拡張し、混合エキスパートネットワークで単一のポリシーが複数の歩容を模倣できるようにする新しい損失関数を提案し、実機で検証した論文。
著者: Alexander Reske, Jan Carius, Yuntao Ma, Farbod Farshidian, Marco Hutter
分類: cs.RO, cs.AI, cs.SY, eess.SY
原文アブストラクト
We present a learning algorithm for training a single policy that imitates multiple gaits of a walking robot. To achieve this, we use and extend MPC-Net, which is an Imitation Learning approach guided by Model Predictive Control (MPC). The strategy of MPC-Net differs from many other approaches since its objective is to minimize the control Hamiltonian, which derives from the principle of optimality. To represent the policies, we employ a mixture-of-experts network (MEN) and observe that the performance of a policy improves if each expert of a MEN specializes in controlling exactly one mode of a hybrid system, such as a walking robot. We introduce new loss functions for single- and multi-gait policies to achieve this kind of expert selection behavior. Moreover, we benchmark our algorithm against Behavioral Cloning and the original MPC implementation on various rough terrain scenarios. We validate our approach on hardware and show that a single learned policy can replace its teacher to control multiple gaits.
