Loss Regularizing Robotic Terrain Classification

arXiv:2403.13695v1 Announce Type: cross
Abstract: 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 …

abstract accuracy arxiv classification classifiers cs.lg cs.ro legged robots loss low movements overfitting robotic robots through type