Loss Regularizing Robotic Terrain Classification
Shakti Deo Kumar Sudhanshu Tripathi Krishna Ujjwal Sarvada Sakshi Jha Suddhasil De
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.