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

2026-10-03

AI Summary

This study addresses the limitation of fixed-category classification in outdoor terrain perception for ground robots, which hinders scalability to novel classes. To overcome this, we propose an open-set surface classification method based on temporal state prediction and attention mechanisms. The core innovation lies in parameterizing novel surface categories as trainable vectors, enabling dynamic class addition and thereby transcending traditional closed-set constraints. Experimental evaluations demonstrate that the proposed approach achieves classification accuracies of 98.56% and 94.8% on the Belyaev-Kushnarev and BorealTC datasets, respectively. These results confirm the method's capability to deliver high-precision and scalable open-world terrain perception for autonomous ground navigation.

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.

Problem

• Surface Classification
• Robot State Prediction
• Terrain Understanding
• Open-Ended Learning
• Ground Robots

Innovation

• Attention Mechanism
• Surface Representation Learning
• Open-Ended Classification
• Robot State Prediction
• Trainable Vectors