# 用于机器人状态预测和开放式表面分类的基于注意力的表面表示学习

> 原文链接：https://robotics.hellobike.com/papers/2026-10-03-2610-04240
> 频道：papers | 更新日期：2026-10-03 | 标签：arxiv, cs.RO
> 核心摘要：针对户外地面机器人地形类别固定问题，提出结合下一状态预测与注意力块的方法，并在两个数据集上验证分类。

# 用于机器人状态预测和开放式表面分类的基于注意力的表面表示学习

::: v-pre
> 原文链接：[Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification](https://arxiv.org/abs/2610.04240)（arXiv 2610.04240 · 发布日期：2026-10-03）

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

针对户外地面机器人地形类别固定问题，提出结合下一状态预测与注意力块的方法，并在两个数据集上验证分类。

## 核心要点

- 户外地面机器人需理解底层地形属性，以保障可靠运行。
- 方法将新表面类别加入为可训练向量，用于后续高层任务。
- 学习范式预测机器人下一时刻状态，并引入注意力块。
- 在Belyaev-Kushnarev与BorealTC上准确率达98.56%和94.8%。

**验证范围与局限**：验证限于Belyaev-Kushnarev和BorealTC数据集上的分类准确率。

## 英文摘要 (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.

作者：Oleg Kushnarev, Alexander Belyaev
:::

## 原始参考来源 (Sources)

- [arXiv 2610.04240：Attention-Based Surface Representation Learning for Robot State Prediction and Open-Ended Surface Classification](https://arxiv.org/abs/2610.04240)