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

arXiv: https://arxiv.org/abs/2610.04240 · HTML: https://mlsanity.com/paper/2610.04240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 3 Oct 2026.

## Builds on (most cited first, 7 of 7)

- [Prototypical Contrastive Learning of Unsupervised Representations](https://mlsanity.com/paper/2005.04966/) (2020)
- [Self-Supervised Visual Terrain Classification from Unsupervised Acoustic Feature Learning](https://mlsanity.com/paper/1912.03227/) (2019)
- [VINet: Visual and Inertial-based Terrain Classification and Adaptive Navigation over Unknown Terrain](https://mlsanity.com/paper/2209.07725/) (2022)
- [Surface Type Classification for Autonomous Robot Indoor Navigation](https://mlsanity.com/paper/1905.00252/) (2019)
- [Proprioception Is All You Need: Terrain Classification for Boreal Forests](https://mlsanity.com/paper/2403.16877/) (2024)
- [Conv1D Energy-Aware Path Planner for Mobile Robots in Unstructured Environments](https://mlsanity.com/paper/2104.01560/) (2021)
- [Learning Surface Terrain Classifications from Ground Penetrating Radar](https://mlsanity.com/paper/2404.09094/) (2024)

## Abstract (arXiv)

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.