pith. sign in

arxiv: 2606.18698 · v1 · pith:MNBJIQ3Unew · submitted 2026-06-17 · 💻 cs.RO · cs.AI· cs.LG

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

Alexander Belyaev , Oleg Kushnarev This is my paper

Pith reviewed 2026-06-26 20:54 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.LG
keywords surface classificationenergy featuresdeep learningmobile roboticsinertial dataconvolutional neural networksaccuracy improvementstate-space models
0
0 comments X

The pith

Energy features enable 85-90% surface classification on their own and add 1-2% to inertial models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests energy-derived features as a way to classify surfaces for mobile robots, either alone or added to inertial measurements. Across three public datasets and several deep learning architectures tuned automatically, energy features alone reach 85-90% accuracy. Adding them to inertial data lifts accuracy to 96-99% with a steady 1-2% mean gain and beats earlier reported numbers on every dataset. The convolutional network performs best overall. These outcomes indicate energy features work as a practical standalone option and as a useful supplement.

Core claim

The authors demonstrate that energy-based features extracted from sensor data enable surface classification accuracies of 85-90% when used in isolation with modern deep learning architectures, and that augmenting inertial features with these energy features produces a consistent 1-2% mean accuracy improvement, reaching 96-99% overall, outperforming prior reported values on the evaluated datasets.

What carries the argument

Energy-derived features used as input to deep learning models (RNNs, CNNs, encoder-only transformers, Mamba) after automated hyperparameter tuning and sequence-length optimization.

If this is right

  • Classifiers using only energy features reach sufficient accuracy for standalone deployment in surface classification.
  • Augmenting inertial data with energy features produces consistent 1-2% accuracy gains across datasets.
  • Convolutional neural networks yield the highest performance among the tested architectures.
  • Modern deep learning models with automated tuning exceed previously reported accuracies on all three datasets.
  • Energy features function as a viable supplementary sensing modality alongside inertial data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Robots might reduce sensor count or power use if energy features alone prove reliable in new environments.
  • The complementary gain suggests energy features capture surface information that inertial measurements miss.
  • The same augmentation approach could be tested on other robotics perception tasks such as terrain or object identification.
  • Results may depend on the specific datasets; validation on additional real-world robot traces would strengthen the findings.

Load-bearing premise

The accuracy gains from adding energy features come from the features themselves rather than from the automated hyperparameter search or the choice of modern architectures.

What would settle it

Running the identical architectures and hyperparameter search on the same datasets but without energy features and obtaining equal or higher accuracy would falsify the claim that the gains are attributable to the energy features.

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper evaluates energy-derived features for surface classification in mobile robotics, either standalone or combined with inertial data. Across three public datasets, modern DL models (RNN, CNN, transformer, Mamba) with automated hyperparameter tuning and sequence-length optimization achieve 85-90% accuracy on energy features alone (5-10% below inertial+energy at 96-99%) and a consistent 1-2% mean accuracy gain when energy features augment inertial inputs. The work also reports higher accuracies than prior published numbers on the same datasets.

Significance. If the reported 1-2% gains can be isolated to the energy features rather than to architecture choice or hyperparameter search, the result would indicate that energy features provide a low-cost, useful supplementary signal for terrain classification and that energy-only classifiers reach practically usable accuracy. The multi-dataset, multi-architecture design with automated tuning is a methodological strength that supports broader claims about feature utility.

major comments (3)
  1. [Methods] Methods (hyperparameter tuning and input optimization paragraph): the automated search is described as being performed for each input configuration, but it is not stated whether the search budget, trial count, or search space is held identical across inertial-only, energy-only, and combined conditions. Independent optimization per condition confounds attribution of the 1-2% lift to the energy features themselves.
  2. [Results] Results (accuracy tables and text reporting 1-2% mean improvement): no standard deviations, error bars, or statistical significance tests (e.g., paired t-test or Wilcoxon) are provided for the accuracy differences. Without these, it is impossible to assess whether the reported 1-2% mean improvement exceeds experimental noise.
  3. [Abstract and Results] Abstract and Results (comparison to prior reported values): the claim that the models exceed previously published accuracies is not isolated from the change in model families (CNN/transformer/Mamba) and the use of automated tuning; the comparison therefore does not establish that energy features alone drive the improvement over earlier work.
minor comments (2)
  1. [Methods] The exact mathematical definition or preprocessing steps used to compute the energy features are not given in the main text; a short appendix equation or pseudocode would improve reproducibility.
  2. [Figures and Tables] Figure captions and table headers should explicitly state whether reported numbers are means over multiple random seeds or single runs.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review. The comments highlight important aspects of experimental design and reporting that we address point-by-point below. We believe these clarifications and additions will strengthen the manuscript without altering its core findings.

read point-by-point responses

  1. Referee: [Methods] Methods (hyperparameter tuning and input optimization paragraph): the automated search is described as being performed for each input configuration, but it is not stated whether the search budget, trial count, or search space is held identical across inertial-only, energy-only, and combined conditions. Independent optimization per condition confounds attribution of the 1-2% lift to the energy features themselves.

    Authors: We agree that explicit confirmation of identical search budgets is necessary to support attribution of performance differences to the input features. The automated tuning procedure (Optuna with the same number of trials, identical search space definitions for learning rate, hidden size, layers, etc., and the same early-stopping criteria) was in fact applied uniformly across all three input conditions. We will revise the Methods section to state this explicitly, including the exact trial count and search-space bounds used for every configuration. revision: yes

  2. Referee: [Results] Results (accuracy tables and text reporting 1-2% mean improvement): no standard deviations, error bars, or statistical significance tests (e.g., paired t-test or Wilcoxon) are provided for the accuracy differences. Without these, it is impossible to assess whether the reported 1-2% mean improvement exceeds experimental noise.

    Authors: We concur that variability measures and statistical tests are required to evaluate whether the 1-2% gains are meaningful. In the revised manuscript we will report standard deviations computed over the multiple random seeds / cross-validation folds already performed, add error bars to the accuracy tables and figures, and include paired Wilcoxon signed-rank tests (with p-values) comparing inertial-only versus inertial+energy results on each dataset. revision: yes

  3. Referee: [Abstract and Results] Abstract and Results (comparison to prior reported values): the claim that the models exceed previously published accuracies is not isolated from the change in model families (CNN/transformer/Mamba) and the use of automated tuning; the comparison therefore does not establish that energy features alone drive the improvement over earlier work.

    Authors: Our statements in the abstract and results section report that the evaluated models achieve higher accuracy than previously published numbers on the same datasets; we do not attribute those gains to energy features. To prevent misinterpretation we will revise the relevant sentences to explicitly note that the higher accuracies result from the use of modern architectures together with automated hyperparameter and sequence-length optimization, while the primary scientific contribution remains the systematic evaluation of energy features both alone and in combination with inertial data. revision: partial

Circularity Check

0 steps flagged

No significant circularity; empirical results on held-out data

full rationale

The paper is an empirical evaluation of deep learning models on three public datasets for surface classification. Central claims consist of reported accuracies (85-90% for energy features alone, 96-99% combined, 1-2% lift when augmenting inertial data) obtained from training RNN/CNN/transformer/Mamba models under automated hyperparameter tuning. No equations, fitted parameters, or self-citations are present that reduce these performance numbers to quantities defined by the authors' own prior choices or inputs. The derivation chain is self-contained because results are measured on independent test sets against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central empirical claims rest on the assumption that the three public datasets are representative and that automated hyperparameter tuning produces fair comparisons. No new physical axioms or invented entities are introduced. Free parameters consist of the many hyperparameters searched during training and the choice of input sequence length.

free parameters (2)
  • model hyperparameters
    Automated tuning searches learning rates, layer sizes, and other settings for each architecture and dataset.
  • input sequence length
    The paper optimizes sequence length as part of the experimental protocol.
axioms (1)
  • domain assumption Standard deep learning optimization converges to useful classifiers on the given sensor data.
    Invoked implicitly when reporting that modern architectures achieve the stated accuracies.

pith-pipeline@v0.9.1-grok · 5731 in / 1427 out tokens · 22275 ms · 2026-06-26T20:54:07.116133+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

39 extracted references · 38 canonical work pages · 3 internal anchors

  1. [1]

    doi: 10.1002/rob.21761

    work page doi:10.1002/rob.21761

  2. [2]

    TABLE IV RESULTS ON BOREALTC DATASET Feature set RNN CNN Mamba Transf

    doi: 10.1002/rob.21408. TABLE IV RESULTS ON BOREALTC DATASET Feature set RNN CNN Mamba Transf. Mean Set 1 0.9236 0.9191 0.9140 0.8571 0.9035 Set 2 0.9235 0.9183 0.9180 0.8547 0.9036 Set 3 0.8408 0.8443 0.8266 0.7529 0.8161 Set 4 0.9435 0.9587 0.9548 0.8997 0.9391 Set 5 0.9619 0.9580 0.9621 0.9477 0.9574 Set 6 0.9564 0.9678 0.9463 0.9233 0.9485 Mean 0.9249...

    work page doi:10.1002/rob.21408

  3. [3]

    doi: 10.1109/LRA.2016.2525040

    work page doi:10.1109/lra.2016.2525040 2016

  4. [4]

    doi: 10.1109/TRO.2020.3031214

    work page doi:10.1109/tro.2020.3031214 2020

  5. [5]

    doi: 10.1109/ACCESS.2021.3059620

    work page doi:10.1109/access.2021.3059620 2021

  6. [6]

    In: Proc

    T. Guan, R. Song, Z. Ye, and L. Zhang, “VINet: Visual and inertial-based terrain classification and adaptive navigation over unknown terrain,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), London, UK, 2023, pp. 4106–4112. doi: 10.1109/ICRA48891.2023.10161251

    work page doi:10.1109/icra48891.2023.10161251 2023

  7. [7]

    Deep Learning based Semantic Segmentation for Mars Rover Terrain Classification,

    F. Mohammad, Y. Gao, S. Kay, R. Field, M. De Benedetti, and E. V. Ntagiou, “Deep Learning based Semantic Segmentation for Mars Rover Terrain Classification,” in 2024 International Conference on Space Robotics (iSpaRo), Luxembourg, Luxembourg: IEEE, June 2024, pp. 292–298. doi: 10.1109/iSpaRo60631.2024.10687827

    work page doi:10.1109/isparo60631.2024.10687827 2024

  8. [8]

    Surface Type Classification for Autonomous Robot Indoor Navigation

    F. Lomio, E. Skenderi, D. Mohamadi, J. Collin, R. Ghabcheloo, and H. Huttunen, “Surface Type Classification for Autonomous Robot Indoor Navigation,” 2019, doi: 10.48550/ARXIV.1905.00252

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1905.00252 2019

  9. [9]

    Robot ground classification and recognition based on CNN-LSTM model,

    X. Li, J. Wu, Z. Li, Z. Chen, and L. Zhang, “Robot ground classification and recognition based on CNN-LSTM model,” in Proc. IEEE Int. Conf. Big Data, Artif. Intell. Internet Things Eng. (ICBAIE), Nanchang, China, 2021, pp. 1110–1113. doi: 10.1109/ICBAIE52039.2021.9389912

    work page doi:10.1109/icbaie52039.2021.9389912 2021

  10. [10]

    doi: 10.3390/machines13030251

    work page doi:10.3390/machines13030251

  11. [11]

    doi: 10.1007/s11063-024-11679-w

    work page doi:10.1007/s11063-024-11679-w

  12. [12]

    doi: 10.18280/jesa.570610

    work page doi:10.18280/jesa.570610

  13. [13]

    Adaptive Domain- Enhanced Transfer Learning for Welding Defect Classification

    S. Satsevich et al., “HyperSurf: Quadruped Robot Leg Capable of Surface Recognition with GRU and Real-to-Sim Transferring,” in 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia: IEEE, Oct. 2024, pp. 2625–2630. doi: 10.1109/SMC54092.2024.10831295

    work page doi:10.1109/smc54092.2024.10831295 2024

  14. [14]

    doi: 10.3390/electronics12153238

    work page doi:10.3390/electronics12153238

  15. [15]

    doi: 10.3390/s19051137

    work page doi:10.3390/s19051137

  16. [16]

    doi: 10.1109/LRA.2022.3158462

    work page doi:10.1109/lra.2022.3158462 2022

  17. [17]

    Where am I walking? – MultiNet-based proprioceptive terrain classification for legged robots,

    L. Puck, M. Krause, T. Schnell, T. Bertram, and D. Abel, “Where am I walking? – MultiNet-based proprioceptive terrain classification for legged robots,” in Proc. Int. Conf. Ubiquitous Robot. (UR), Honolulu, HI, USA, 2023, pp. 313–318. doi: 10.1109/UR57808.2023.10202428

    work page doi:10.1109/ur57808.2023.10202428 2023

  18. [18]

    Let-3d-ap: Longitudinal error tolerant 3d average precision for camera-only 3d detection

    X. Liu, H. Chen, and H. Chen, “Contrastive Learning-Based Attribute Extraction Method for Enhanced Terrain Classification,” in 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan: IEEE, May 2024, pp. 5644–5650. doi: 10.1109/ICRA57147.2024.10611271

    work page doi:10.1109/icra57147.2024.10611271 2024

  19. [19]

    doi: 10.3390/machines13100900

    work page doi:10.3390/machines13100900

  20. [20]

    doi: 10.1109/TRO.2005.862480

    work page doi:10.1109/tro.2005.862480 2005

  21. [21]

    doi: 10.1002/rob.20113

    work page doi:10.1002/rob.20113

  22. [22]

    doi: 10.3390/machines14010099

    work page doi:10.3390/machines14010099

  23. [23]

    doi: 10.1109/TRO.2025.3534512

    work page doi:10.1109/tro.2025.3534512 2025

  24. [24]

    doi: 10.1109/TIE.2024.3366222

    work page doi:10.1109/tie.2024.3366222 2024

  25. [25]

    doi: 10.1007/s10514-020-09912-5

    work page doi:10.1007/s10514-020-09912-5

  26. [26]

    NNPP: A learning-based heuristic model for accelerating optimal path planning on uneven terrain,

    Y. Ji, Y. Liu, G. Xie, B. Ma, Z. Xie, and B. Cao, “NNPP: A learning-based heuristic model for accelerating optimal path planning on uneven terrain,” Robotics and Autonomous Systems, vol. 193, p. 105084, Nov. 2025, doi: 10.1016/j.robot.2025.105084

    work page doi:10.1016/j.robot.2025.105084 2025

  27. [27]

    Energy-aware terrain analysis for mobile robot exploration,

    K. Otsu and T. Kubota, “Energy-aware terrain analysis for mobile robot exploration,” in Field Serv. Robot., D. Wettergreen and T. Barfoot, Eds. Cham, Switzerland: Springer, 2016, vol. 113, pp. 373–386. doi: 10.1007/978-3-319-27702-8_25

    work page doi:10.1007/978-3-319-27702-8_25 2016

  28. [28]

    doi: 10.1109/TII.2018.2844370

    work page doi:10.1109/tii.2018.2844370 2018

  29. [29]

    In: Proc

    M. Visca, A. Bouton, R. Powell, and Y. Gao, “Conv1D energy-aware path planner for mobile robots in unstructured environments,” in Proc. IEEE Int. Conf. Robot. Autom. (ICRA), Xi’an, China, 2021, pp. 2279–2285. doi: 10.1109/ICRA48506.2021.9560771

    work page doi:10.1109/icra48506.2021.9560771 2021

  30. [30]

    doi: 10.1109/LRA.2021.3130630

    work page doi:10.1109/lra.2021.3130630 2021

  31. [31]

    doi: 10.3390/robotics14060074

    work page doi:10.3390/robotics14060074

  32. [32]

    Ang, and Francis E

    D. LaRocque, W. Guimont-Martin, D.-A. Duclos, P. Giguère, and F. Ferland, “Proprioception is all you need: Terrain classification for boreal forests,” in Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), Abu Dhabi, UAE, 2024, pp. 11686–11693. doi: 10.1109/IROS58592.2024.10801407

    work page doi:10.1109/iros58592.2024.10801407 2024

  33. [33]

    doi: 10.1016/j.engappai.2025.113060

    work page doi:10.1016/j.engappai.2025.113060 2025

  34. [34]

    doi: 10.3390/robotics14090130

    work page doi:10.3390/robotics14090130

  35. [35]

    Learning phrase representations using RNN encoder ⚶decoder for statistical machine translation

    K. Cho et al., “Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar: Association for Computational Linguistics, 2014, pp. 1724–1734. doi: 10.3115/v1/D14-1179

    work page doi:10.3115/v1/d14-1179 2014

  36. [36]

    doi: 10.1016/j.ymssp.2020.107398

    work page doi:10.1016/j.ymssp.2020.107398 2020

  37. [37]

    Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf 8

    [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf 8

    2017

  38. [38]

    Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

    T. Dao and A. Gu, “Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality,” 2024, arXiv. doi: 10.48550/ARXIV.2405.21060

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2405.21060 2024

  39. [39]

    Decoupled Weight Decay Regularization

    I. Loshchilov and F. Hutter, “Decoupled Weight Decay Regularization,” 2017, arXiv. doi: 10.48550/ARXIV.1711.05101. 9 TABLE A-I CLASSIFICATION REPORT FOR THE BEST NET (RNN, SET 2 KALMAN) ON BELYAEV-KUSHNAREV DATASET Surface Precision Recall F1-score red 1.0000 1.0000 1.0000 gray 0.9880 0.9533 0.9703 green 0.9539 0.9881 0.9707 table 1.0000 1.0000 1.0000 mac...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.1711.05101 2017