脚式ロボットの地形分類のための半教師ありゲート付きリカレントニューラルネットワーク
Semi-supervised Gated Recurrent Neural Networks for Robotic Terrain Classification
ゲート付きリカレントニューラルネットワークを用いて、脚式ロボットが走行する地形を教師あり・半教師ありの両方で分類し、ラベルなしデータの活用で精度を大幅に向上させた研究。
著者: Ahmadreza Ahmadi, Tønnes Nygaard, Navinda Kottege, David Howard, Nicolas Hudson
分類: cs.RO, cs.AI
原文アブストラクト
Legged robots are popular candidates for missions in challenging terrains due to the wide variety of locomotion strategies they can employ. Terrain classification is a key enabling technology for autonomous legged robots, as it allows the robot to harness their innate flexibility to adapt their behaviour to the demands of their operating environment. In this paper, we show how highly capable machine learning techniques, namely gated recurrent neural networks, allow our target legged robot to correctly classify the terrain it traverses in both supervised and semi-supervised fashions. Tests on a benchmark data set shows that our time-domain classifiers are well capable of dealing with raw and variable-length data with small amount of labels and perform to a level far exceeding the frequency-domain classifiers. The classification results on our own extended data set opens up a range of high-performance behaviours that are specific to those environments. Furthermore, we show how raw unlabelled data is used to improve significantly the classification results in a semi-supervised model.
