Pose Consistency KKT-Loss for Weakly Supervised Learning of Robot-Terrain Interaction Model

Abstract

We address the problem of self-supervised learning for predicting the shape of supporting terrain (i.e. the terrain which will provide rigid support for the robot during its traversal) from sparse input measurements. The learning method exploits two types of ground-truth labels: dense 2.5D maps and robot poses, both estimated by a usual SLAM procedure from offline recorded measurements. We show that robot poses are required because straightforward supervised learning from the 3D maps only suffers from: (i) exaggerated height of the supporting terrain caused by terrain flexibility (vegetation, shallow water, snow or sand) and (ii) missing or noisy measurements caused by high spectral absorbance or non-Lambertian reflectance of the measured surface. We address the learning from robot poses by introducing a novel KKT-loss, which emerges as the distance from necessary Karush-Kuhn-Tucker conditions for constrained local optima of a simplified first-principle model of the robot-terrain interaction. We experimentally verify that the proposed weakly supervised learning from ground-truth robot poses boosts the accuracy of predicted support heightmaps and increases the accuracy of estimated robot poses. All experiments are conducted on a dataset captured by a real platform. Both the dataset and codes which replicates experiments in the paper are made publicly available as a part of the submission.