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

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 tasks. By employing a learning paradigm based on predicting the robot's next state in time and using attention blocks, we improved classification accuracy to 98.56% on the Belyaev-Kushnarev dataset and 94.8% on BorealTC.

Robotics (cs.RO); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2610.04240 [cs.RO]

(or arXiv:2610.04240v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.04240 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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