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.

中文翻译：

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半监督门控递归神经网络用于机器人地形分类

腿式机器人由于可以采用多种运动策略，因此是在具有挑战性的地形中执行任务的热门人选。地形分类是自动腿式机器人的关键启用技术，因为它允许机器人利用其固有的灵活性来使其行为适应其操作环境的要求。在本文中，我们展示了强大的机器学习技术（即门控递归神经网络）如何使我们的目标有腿机器人以有监督和半监督的方式正确地对穿越的地形进行分类。在基准数据集上进行的测试表明，我们的时域分类器能够很好地处理带有少量标签的原始和可变长度数据，并且其性能远远超过了频域分类器。我们自己的扩展数据集上的分类结果为这些环境开辟了一系列高性能行为。此外，我们显示了如何使用原始的未标记数据来显着改善半监督模型中的分类结果。