Locomotion mechanics of legged robots are suitable when pacing through difficult terrains. Recognising terrains for such robots are important to fully yoke the versatility of their movements. Consequently, robotic terrain classification becomes significant to classify terrains in real time with high accuracy. The conventional classifiers suffer from overfitting problem, low accuracy problem, high variance problem, and not suitable for live dataset. On the other hand, classifying a growing dataset is difficult for convolution based terrain classification. Supervised recurrent models are also not practical for this classification. Further, the existing recurrent architectures are still evolving to improve accuracy of terrain classification based on live variable-length sensory data collected from legged robots. This paper proposes a new semi-supervised method for terrain classification of legged robots, avoiding preprocessing of long variable-length dataset. The proposed method has a stacked Long Short-Term Memory architecture, including a new loss regularization. The proposed method solves the existing problems and improves accuracy. Comparison with the existing architectures show the improvements.

翻译：腿式机器人在穿越复杂地形时，其运动力学特性具有适用性。为充分发挥其运动灵活性，对这类机器人进行地形识别至关重要。因此，机器人地形分类成为关键任务，需实现高精度实时分类。传统分类器存在过拟合、精度低、方差大等问题，且不适用于实时数据集。另一方面，基于卷积的地形分类难以处理持续增长的数据集。有监督循环模型同样不适用于此类分类。此外，现有循环架构仍在发展中，旨在提升基于腿式机器人实时变长传感数据的地形分类精度。本文提出一种新的半监督腿式机器人地形分类方法，避免了对长变长数据集的预处理。该方法采用堆叠长短期记忆架构，并引入新的损失正则化技术。所提方法解决了现有问题并提升了分类精度，与现有架构的对比实验验证了其改进效果。