4 accepted papers
We investigated the performance of existing semiand fully autonomous methods for controlling flipper-based skid-steer robots. Our study involves the reimplementation of these methods for a fair comparison, and it introduces a novel semi-autonomous control policy that provides a compelling trade-off
Automatic pseudo-labeling is a powerful tool to tap into large amounts of sequential unlabeled data. It is especially appealing in safety-critical applications of autonomous driving, where performance requirements are extreme, datasets are large, and manual labeling is very challenging. We propose t
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
This letter presents a complete pipeline for learning continuous motion control policies for a mobile robot when only a nondifferentiable physics simulator of robot–terrain interactions is available. The multimodal state estimation of the robot is also complex and difficult to simulate, so we simult