Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification
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.
| Authors | Oleg Kushnarev, Alexander Belyaev |
|---|---|
| Primary category | cs.RO |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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