Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification

Excerpt

arXiv:2610.04240v1 Announce Type: cross Abstract: For ground robots operating in outdoor environments, understanding the properties of the underlying terrain is essential for ensuring reliable operation. In most perception-based studies, this problem is formulated as categorical classification with a fixed number of classes defined during training. We propose an approach that enables new surface classes to be added as trainable vectors, which can subsequently be used to address higher-level task