アフィン変換による最適輸送を用いたSim-to-Realドメイン適応
Affine Transport for Sim-to-Real Domain Adaptation
状態遷移分布間の対応をアフィン変換でモデル化する最適輸送の変種を提案し、Procrustes整列で拡張。ホッケーパックを目標位置で止める実機タスクなどでモデル適応誤差を大幅に削減した。
著者: Anton Mallasto, Karol Arndt, Markus Heinonen, Samuel Kaski, Ville Kyrki
分類: cs.RO, cs.LG
原文アブストラクト
Sample-efficient domain adaptation is an open problem in robotics. In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between state transition distributions between the source and target domains with an affine transformation. First, we derive the affine transport framework; then, we extend the basic framework with Procrustes alignment to model arbitrary affine transformations. We evaluate the method in a number of OpenAI Gym sim-to-sim experiments with simulation environments, as well as on a sim-to-real domain adaptation task of a robot hitting a hockeypuck such that it slides and stops at a target position. In each experiment, we evaluate the results when transferring between each pair of dynamics domains. The results show that affine transport can significantly reduce the model adaptation error in comparison to using the original, non-adapted dynamics model.
