高次元状態空間における移動計画のための抽象表現学習
Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces
CNNでコスト関数を表現し、人工データで学習させることで実世界の抽象化にも汎化させ、ハイブリッド走行・歩行移動の探索計画を高速化した研究。
著者: Tobias Klamt, Sven Behnke
分類: cs.RO
原文アブストラクト
Ground robots which are able to navigate a variety of terrains are needed in many domains. One of the key aspects is the capability to adapt to the ground structure, which can be realized through movable body parts coming along with additional degrees of freedom (DoF). However, planning respective locomotion is challenging since suitable representations result in large state spaces. Employing an additional abstract representation---which is coarser, lower-dimensional, and semantically enriched---can support the planning. While a desired robot representation and action set of such an abstract representation can be easily defined, the cost function requires large tuning efforts. We propose a method to represent the cost function as a CNN. Training of the network is done on generated artificial data, while it generalizes well to the abstraction of real world scenes. We further apply our method to the problem of search-based planning of hybrid driving-stepping locomotion. The abstract representation is used as a powerful informed heuristic which accelerates planning by multiple orders of magnitude.
