# Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets

arXiv: https://arxiv.org/abs/2606.18698 · HTML: https://mlsanity.com/paper/2606.18698/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 17 Jun 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality](https://mlsanity.com/paper/2405.21060/) (2024)
- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [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)
- [Predicting Energy Consumption of Ground Robots On Uneven Terrains](https://mlsanity.com/paper/2212.06393/) (2022)
- [NNPP: A Learning-Based Heuristic Model for Accelerating Optimal Path Planning on Uneven Terrain](https://mlsanity.com/paper/2308.04792/) (2023)
- [HyperSurf: Quadruped Robot Leg Capable of Surface Recognition with GRU and Real-to-Sim Transferring](https://mlsanity.com/paper/2407.15622/) (2024)

## Abstract (arXiv)

The energy-based method remains a comparatively underexamined approach for surface classification in mobile robotics, despite promising results in constrained environments. This study evaluated the viability of using energy-derived features as either a standalone classification modality or as supplementary input to inertial data. A comprehensive evaluation was conducted across three publicly available datasets, comparing the performance of modern deep learning architectures including recurrent neural networks, convolutional neural networks, encoder-only transformers, and Mamba state-space models, under automated hyperparameter tuning and input sequence length optimization. The models achieved higher accuracy than previously reported values on all evaluated datasets, with the convolutional neural network yielding the highest overall performance. When relying exclusively on energy-based features, the models attained classification accuracies in the range of 85-90%, approximately 5-10% lower than those achieved when combined with inertial features (96-99%). Augmenting inertial data with energy features resulted in a consistent mean accuracy improvement of 1-2%. These findings indicate that classifiers relying solely on energy features offer sufficient accuracy for standalone deployment, while also providing a consistent gain when used in combination with other sensing modalities.