CNN-based Foothold Selection for Mechanically Adaptive Soft Foot

In this paper, we consider a problem of foothold selection for the quadrupedal robots equipped with compliant adaptive feet. Starting from a model of the foot we compute the quality of the potential footholds considering also kinematic constraints and collisions during evaluation. Since terrain assessment and constraints checking are computationally expensive we applied a Convolutional Neural Network (CNN) to evaluate the potential footholds on the elevation map. We propose an efficient strategy for data clustering and segmentation with CNN. The data for training the neural network is collected off-line but the inference works on-line when the robot walks on rough terrains and allows for efficient adaptation to the terrain and exploitation of the properties of the soft adaptive feet.