最適経路計画のための漸進的敵対的学習
Incremental Adversarial Learning for Optimal Path Planning
経路計画アルゴリズムと識別器の間でゼロサムゲームを構築し、軌道データに暗黙的に含まれる自然な振る舞いを学習しながら経路を反復的に最適化する手法を提案した。
著者: Salvatore Virga, Christian Rupprecht, Nassir Navab, Christoph Hennersperger
分類: cs.RO
原文アブストラクト
Path planning plays an essential role in many areas of robotics. Various planning techniques have been presented, either focusing on learning a specific task from demonstrations or retrieving trajectories by optimizing for hand-crafted cost functions which are well defined a priori. In this work, we present an incremental adversarial learning-based framework that allows inferring implicit behaviour, i.e. the natural characteristic of a set of given trajectories. To achieve adversarial learning, a zero-sum game is constructed between a planning algorithm and an adversary - the discriminator. We employ the discriminator within an optimal motion planning algorithm, such that costs can be learned and optimized iteratively, improving the integration of implicit behavior. By combining a cost-based planning approach with trained intrinsic behaviour, this can be be integrated also with other constraints such as obstacles or general cost factors within a single planning framework. We demonstrate the proposed method on a dataset for collision avoidance, as well as for the generation of human-like trajectories from motion capture data. Our results show that incremental adversarial learning is able to generate paths that reflect the natural implicit behaviour of a dataset, with the ability to improve on performance using iterative learning and generation.
