MAAIP: 物理ベースキャラクターの格闘デモンストレーション模倣のためのマルチエージェント敵対的相互作用事前分布
MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters
単一アクターの動作データと少数の相互作用データから、物理ベースの複数キャラクターが格闘スタイルを保ちつつ相互作用を模倣する制御方策を、マルチエージェント敵対的模倣学習で訓練する手法を提案。
著者: Mohamed Younes, Ewa Kijak, Richard Kulpa, Simon Malinowski, Franck Multon
分類: cs.CV, cs.AI, cs.GR, cs.LG, cs.RO
原文アブストラクト
Simulating realistic interaction and motions for physics-based characters is of great interest for interactive applications, and automatic secondary character animation in the movie and video game industries. Recent works in reinforcement learning have proposed impressive results for single character simulation, especially the ones that use imitation learning based techniques. However, imitating multiple characters interactions and motions requires to also model their interactions. In this paper, we propose a novel Multi-Agent Generative Adversarial Imitation Learning based approach that generalizes the idea of motion imitation for one character to deal with both the interaction and the motions of the multiple physics-based characters. Two unstructured datasets are given as inputs: 1) a single-actor dataset containing motions of a single actor performing a set of motions linked to a specific application, and 2) an interaction dataset containing a few examples of interactions between multiple actors. Based on these datasets, our system trains control policies allowing each character to imitate the interactive skills associated with each actor, while preserving the intrinsic style. This approach has been tested on two different fighting styles, boxing and full-body martial art, to demonstrate the ability of the method to imitate different styles.