pith. machine review for the scientific record. sign in

arxiv: 2604.12905 · v1 · submitted 2026-04-14 · 💻 cs.RO · cs.LG

Recognition: unknown

Frequency-aware Decomposition Learning for Sensorless Wrench Forecasting on a Vibration-rich Hydraulic Manipulator

Daegil Park, Hyeonbeen Lee, Jin-Gyun Kim, Jong-Boo Han, Min-Jae Jung, Tae-Kyeong Yeu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:22 UTC · model grok-4.3

classification 💻 cs.RO cs.LG
keywords wrench estimationsensorless force predictionfrequency decompositionhydraulic manipulatortransfer learningrobotic interactionvibration forecastingproprioceptive sensing
0
0 comments X

The pith

A frequency-aware neural network forecasts high-frequency wrench components more accurately than standard methods in vibration-rich robotic tasks.

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

The paper introduces a method to estimate forces and torques without physical sensors by predicting them from the robot's own joint movements. It focuses on tasks with rapid vibrations, such as grinding, where high-frequency components matter. By breaking down the wrench signal into frequency bands and treating the high-frequency part as a learned probability distribution, the approach adapts to different frequency ranges. This matters because physical sensors add size, cost, and fragility, so better sensorless methods could enable more robust robot-environment interactions in dynamic settings.

Core claim

The Frequency-aware Decomposition Network (FDN) predicts spectrally decomposed wrench signals from proprioceptive history using asymmetric deterministic and probabilistic heads for low and high frequencies, along with learned frequency filtering and band priors, and demonstrates superior performance in the high-frequency band on real grinding data from a hydraulic manipulator after pretraining and transfer.

What carries the argument

The Frequency-aware Decomposition Network (FDN), which decomposes wrench prediction into frequency bands with adaptive filtering and probabilistic modeling of high-frequency residuals.

If this is right

  • Sensorless wrench forecasting becomes feasible for high-speed interactions without relying on fragile hardware sensors.
  • Pretraining on large robot datasets followed by transfer learning yields better generalization to specific manipulators and tasks.
  • Modeling high-frequency wrench as a conditional distribution rather than point estimates captures uncertainty in vibrations.
  • Frequency-band specific evaluation reveals where improvements are most needed in robotic estimation.

Where Pith is reading between the lines

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

  • The same decomposition strategy could apply to other sensorless estimation problems like velocity or position forecasting in dynamic environments.
  • Separate control loops might use the low-frequency deterministic predictions for planning and high-frequency distributions for compliance or damping.
  • Scaling pretraining to even larger and more diverse robot datasets might further reduce the need for task-specific data collection.

Load-bearing premise

The robot's internal joint states over time contain enough information to predict the high-frequency parts of external forces and torques, and the frequency-specific patterns learned from pretraining data apply to new hydraulic manipulator tasks.

What would settle it

A direct comparison of high-frequency band prediction errors between FDN and baseline methods on the grinding excavation dataset from the 6-DoF hydraulic manipulator would falsify the claim if FDN shows no improvement or higher errors.

Figures

Figures reproduced from arXiv: 2604.12905 by Daegil Park, Hyeonbeen Lee, Jin-Gyun Kim, Jong-Boo Han, Min-Jae Jung, Tae-Kyeong Yeu.

Figure 1
Figure 1. Figure 1: Illustration of the proposed Frequency-aware Decomposition Network (FDN). [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Detailed visualization of frequency-aware layers. FFT and IFFT denote the Fast Fourier Transform and its inverse. (Left) A learnable frequency [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of our hydraulic manipulator and grinding excavation. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualization of the collected hydraulic dataset. The upper two panels [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Energy spectrum of raw wrench windows. Each channel is normalized [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Test episode reconstructions with tdelay = 100 ms. We visualize the fx and my, which are representative channels in our experimental setting described in Section IV-A. The upper two rows correspond to the ‘Soft-1’ episode, and the lower rows correspond to the ‘Stiff-1’ episode. Colored areas illustrate the prediction interval defined by µ ± 3σ. metrics by around 10%, suggesting that the heterogeneous input… view at source ↗
read the original abstract

Force and torque (F/T) sensing is critical for robot-environment interaction, but physical F/T sensors impose constraints in size, cost, and fragility. To mitigate this, recent studies have estimated force/wrench sensorlessly from robot internal states. While existing methods generally target relatively slow interactions, tasks involving rapid interactions, such as grinding, can induce task-critical high-frequency vibrations, and estimation in such robotic settings remains underexplored. To address this gap, we propose a Frequency-aware Decomposition Network (FDN) for short-term forecasting of vibration-rich wrench from proprioceptive history. FDN predicts spectrally decomposed wrench with asymmetric deterministic and probabilistic heads, modeling the high-frequency residual as a learned conditional distribution. It further incorporates frequency-awareness to adaptively enhance input spectra with learned filtering and impose a frequency-band prior on the outputs. We pretrain FDN on a large-scale open-source robot dataset and transfer the learned proprioception-to-wrench representation to the downstream. On real-world grinding excavation data from a 6-DoF hydraulic manipulator and under a delayed estimation setting, FDN outperforms baseline estimators and forecasters in the high-frequency band and remains competitive in the low-frequency band. Transfer learning provides additional gains, suggesting the potential of large-scale pretraining and transfer learning for robotic wrench estimation. Code and data will be made available upon acceptance.

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 / 1 minor

Summary. The paper introduces the Frequency-aware Decomposition Network (FDN) for short-term forecasting of vibration-rich wrench from proprioceptive history on a 6-DoF hydraulic manipulator. FDN performs spectral decomposition of the wrench signal using asymmetric deterministic and probabilistic heads (with the high-frequency residual modeled as a learned conditional distribution), incorporates learned frequency filtering and band priors, and uses pretraining on a large open-source robot dataset followed by transfer to the target platform. On real-world grinding excavation data under delayed estimation, the method is claimed to outperform baselines in the high-frequency band while remaining competitive in the low-frequency band.

Significance. If the empirical results hold under rigorous verification, the work could meaningfully advance sensorless wrench estimation for high-frequency interaction tasks such as grinding and excavation. The combination of frequency-aware decomposition, probabilistic modeling of residuals, and large-scale pretraining with transfer learning represents a promising direction that may reduce dependence on fragile physical F/T sensors while improving robustness in vibration-rich settings.

major comments (3)
  1. Abstract: The central claim that FDN 'outperforms baseline estimators and forecasters in the high-frequency band' is unsupported by any reported quantitative metrics (e.g., RMSE, MAE), baseline specifications, statistical tests, error bars, or ablation results, rendering the primary empirical contribution unverifiable from the provided text.
  2. Methods (FDN architecture and frequency-awareness): The frequency-aware decomposition and probabilistic high-frequency head presuppose that sufficient high-frequency wrench content is observable and learnable from proprioceptive history alone, yet no observability analysis, coherence spectra, or bandwidth characterization between joint encoders/torque sensors and external vibrations is supplied to address hydraulic low-pass filtering effects.
  3. Experiments/transfer learning: The reported gains from pretraining and transfer are stated without details on dataset sizes, domain-shift quantification, or numerical improvement magnitudes relative to training from scratch, which is load-bearing for the claim that large-scale pretraining benefits robotic wrench forecasting.
minor comments (1)
  1. Abstract: The phrase 'delayed estimation setting' is introduced without a definition, delay magnitude, or reference to how the delay is incorporated into the input history or loss.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback, which identifies key areas where additional rigor and transparency will strengthen the manuscript. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: Abstract: The central claim that FDN 'outperforms baseline estimators and forecasters in the high-frequency band' is unsupported by any reported quantitative metrics (e.g., RMSE, MAE), baseline specifications, statistical tests, error bars, or ablation results, rendering the primary empirical contribution unverifiable from the provided text.

    Authors: We agree that the abstract should provide verifiable quantitative support for the central claim. In the revised manuscript we will update the abstract to report specific RMSE and MAE values (with error bars) for the high-frequency band, explicitly name the baseline estimators and forecasters, and note the statistical significance of the observed improvements. The detailed tables, figures, and ablation studies already appear in Section 5; the abstract revision will simply surface the key numbers so the primary contribution is immediately verifiable. revision: yes

  2. Referee: Methods (FDN architecture and frequency-awareness): The frequency-aware decomposition and probabilistic high-frequency head presuppose that sufficient high-frequency wrench content is observable and learnable from proprioceptive history alone, yet no observability analysis, coherence spectra, or bandwidth characterization between joint encoders/torque sensors and external vibrations is supplied to address hydraulic low-pass filtering effects.

    Authors: This observation is correct and highlights a missing analytical component. While the method is data-driven and its effectiveness is demonstrated empirically on real grinding data, we will add coherence spectra and frequency-response characterizations between the proprioceptive inputs (joint encoders and torque sensors) and the target wrench signals. These analyses will be inserted into the revised Methods or Experiments section to quantify observable bandwidth and explicitly address the impact of hydraulic low-pass filtering. revision: yes

  3. Referee: Experiments/transfer learning: The reported gains from pretraining and transfer are stated without details on dataset sizes, domain-shift quantification, or numerical improvement magnitudes relative to training from scratch, which is load-bearing for the claim that large-scale pretraining benefits robotic wrench forecasting.

    Authors: We accept that greater detail is required to substantiate the transfer-learning claims. The revised manuscript will report the exact sizes of the pretraining corpus and the target grinding dataset, provide a quantitative measure of domain shift (e.g., via statistical distances on proprioceptive and wrench distributions), and include numerical deltas (percentage or absolute improvements in RMSE/MAE) between the pretrained model and an identical architecture trained from scratch on the target data alone. These additions will appear in the transfer-learning subsection of the Experiments. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical pretraining and transfer learning with independent validation

full rationale

The paper describes an empirical machine-learning pipeline: pretrain FDN on a large open-source robot dataset, then transfer the proprioception-to-wrench mapping to a new hydraulic manipulator under delayed estimation. Performance is measured by direct comparison against baseline estimators and forecasters on real-world grinding data, with separate reporting for high- and low-frequency bands. No equations, uniqueness theorems, or self-citations are invoked that would make any claimed forecasting result equivalent to a fitted parameter or input by construction. The frequency-aware decomposition, asymmetric heads, and learned priors are architectural choices whose outputs are evaluated externally rather than defined to reproduce the inputs. The central claim therefore rests on observable transfer performance rather than tautological reduction.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

The central claim rests on empirical validation of a new neural architecture whose effectiveness depends on data-driven fitting and transfer; no explicit mathematical axioms or invented physical entities are stated.

free parameters (1)
  • FDN network weights and hyperparameters
    Learned during pretraining on large-scale dataset and fine-tuning on target grinding data; central performance depends on these fitted values.
axioms (1)
  • domain assumption Proprioceptive signals encode sufficient information for short-term wrench forecasting
    Implicit in the sensorless estimation setup and delayed prediction task.
invented entities (1)
  • Frequency-aware Decomposition Network (FDN) no independent evidence
    purpose: To perform spectrally decomposed wrench forecasting with adaptive frequency handling
    New model architecture introduced for this task

pith-pipeline@v0.9.0 · 5564 in / 1298 out tokens · 62624 ms · 2026-05-10T15:22:12.541846+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

32 extracted references · 2 canonical work pages · 2 internal anchors

  1. [1]

    Force feedback plays a significant role in minimally invasive surgery: results and analysis,

    G. Tholey, J. P. Desai, and A. E. Castellanos, “Force feedback plays a significant role in minimally invasive surgery: results and analysis,” Annals of surgery, vol. 241, no. 1, pp. 102–109, 2005

    2005

  2. [2]

    Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback,

    C. Gonz ´alez, J. E. Solanes, A. Munoz, L. Gracia, V . Girb ´es-Juan, and J. Tornero, “Advanced teleoperation and control system for industrial robots based on augmented virtuality and haptic feedback,”Journal of Manufacturing Systems, vol. 59, pp. 283–298, 2021

    2021

  3. [3]

    Force control,

    L. Villani and J. De Schutter, “Force control,” inSpringer handbook of robotics. Springer, 2016, pp. 195–220

    2016

  4. [4]

    A system for imitation learning of contact-rich bimanual manipulation policies,

    S. Stepputtis, M. Bandari, S. Schaal, and H. B. Amor, “A system for imitation learning of contact-rich bimanual manipulation policies,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022, pp. 11 810–11 817

    2022

  5. [5]

    Six-axis force/torque sensors for robotics applications: A review,

    M. Y . Cao, S. Laws, and F. R. y Baena, “Six-axis force/torque sensors for robotics applications: A review,”IEEE Sensors Journal, vol. 21, no. 24, pp. 27 238–27 251, 2021

    2021

  6. [6]

    Neural-network- based contact force observers for haptic applications,

    A. C. Smith, F. Mobasser, and K. Hashtrudi-Zaad, “Neural-network- based contact force observers for haptic applications,”IEEE Transac- tions on Robotics, vol. 22, no. 6, pp. 1163–1175, 2006

    2006

  7. [7]

    An overview of dynamic parameter iden- tification of robots,

    J. Wu, J. Wang, and Z. You, “An overview of dynamic parameter iden- tification of robots,”Robotics and computer-integrated manufacturing, vol. 26, no. 5, pp. 414–419, 2010

    2010

  8. [8]

    Two numerical issues in simulating constrained robot dynamics,

    R. E. Ellis and S. L. Ricker, “Two numerical issues in simulating constrained robot dynamics,”IEEE Transactions on Systems, Man, and Cybernetics, vol. 24, no. 1, pp. 19–27, 2002

    2002

  9. [9]

    Rbf-neural-network- based adaptive robust control for nonlinear bilateral teleoperation ma- nipulators with uncertainty and time delay,

    Z. Chen, F. Huang, W. Sun, J. Gu, and B. Yao, “Rbf-neural-network- based adaptive robust control for nonlinear bilateral teleoperation ma- nipulators with uncertainty and time delay,”Ieee/Asme Transactions on Mechatronics, vol. 25, no. 2, pp. 906–918, 2019

    2019

  10. [10]

    A sensorless interaction forces estimator for bilateral teleoperation system based on online sparse gaussian pro- cess regression,

    A. Dong, Z. Du, and Z. Yan, “A sensorless interaction forces estimator for bilateral teleoperation system based on online sparse gaussian pro- cess regression,”Mechanism and Machine Theory, vol. 143, p. 103620, 2020

    2020

  11. [11]

    End-effector force and joint torque estimation of a 7-dof robotic manipulator using deep learning,

    S. Kru ˇzi´c, J. Musi ´c, R. Kamnik, and V . Papi ´c, “End-effector force and joint torque estimation of a 7-dof robotic manipulator using deep learning,”Electronics, vol. 10, no. 23, p. 2963, 2021

    2021

  12. [12]

    A graph robot network for force observer of teleoperation systems,

    M.-Z. Pan, J.-A. Li, Z. Li, K. Liang, T.-C. Su, K. Liang, and G.-B. Bian, “A graph robot network for force observer of teleoperation systems,” IEEE/ASME Transactions on Mechatronics, vol. 30, no. 1, pp. 530–540, 2024

    2024

  13. [13]

    Fedformer: Frequency enhanced decomposed transformer for long-term series fore- casting,

    T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin, “Fedformer: Frequency enhanced decomposed transformer for long-term series fore- casting,” inInternational conference on machine learning. PMLR, 2022, pp. 27 268–27 286

    2022

  14. [14]

    A time series is worth 64 words: Long-term forecasting with transformers,

    Y . Nie, N. H. Nguyen, P. Sinthong, and J. Kalagnanam, “A time series is worth 64 words: Long-term forecasting with transformers,” in International Conference on Learning Representations, 2023

    2023

  15. [15]

    Rt-2: Vision-language-action models transfer web knowledge to robotic control,

    B. Zitkovich, T. Yu, S. Xu, P. Xu, T. Xiao, F. Xia, J. Wu, P. Wohlhart, S. Welker, A. Wahidet al., “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” inConference on Robot Learning. PMLR, 2023, pp. 2165–2183. PREPRINT NOT PEER REVIEWED 11

    2023

  16. [16]

    Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot,

    H.-S. Fang, H. Fang, Z. Tang, J. Liu, C. Wang, J. Wang, H. Zhu, and C. Lu, “Rh20t: A comprehensive robotic dataset for learning diverse skills in one-shot,” in2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024, pp. 653–660

    2024

  17. [17]

    Grinding force estimation and control of grinding robot with variable impedance control strategy,

    D. Xu, L. Yin, and J. Wang, “Grinding force estimation and control of grinding robot with variable impedance control strategy,”The Interna- tional Journal of Advanced Manufacturing Technology, vol. 137, no. 3, pp. 2011–2027, 2025

    2011

  18. [18]

    Accurate milling force estimation and surgical state recognition in robot-assisted laminectomy,

    J. Hu, Z. Zhou, G. Xia, Y . Dai, J. Zhang, G. Yang, X. Han, J. Jiang, and Y . Liu, “Accurate milling force estimation and surgical state recognition in robot-assisted laminectomy,”Measurement, vol. 253, p. 117673, 2025

    2025

  19. [19]

    Contact force detection and control for robotic polishing based on joint torque sensors,

    Y . Dong, T. Ren, K. Hu, D. Wu, and K. Chen, “Contact force detection and control for robotic polishing based on joint torque sensors,”The International Journal of Advanced Manufacturing Technology, vol. 107, no. 5, pp. 2745–2756, 2020

    2020

  20. [20]

    Dynamic improvement of hydraulic excavator using pressure feedback and gain scheduled model predictive control,

    J. T. Jose, J. Das, and S. K. Mishra, “Dynamic improvement of hydraulic excavator using pressure feedback and gain scheduled model predictive control,”IEEE Sensors Journal, vol. 21, no. 17, pp. 18 526–18 534, 2021

    2021

  21. [21]

    On the spectral bias of neural networks,

    N. Rahaman, A. Baratin, D. Arpit, F. Draxler, M. Lin, F. Hamprecht, Y . Bengio, and A. Courville, “On the spectral bias of neural networks,” inInternational conference on machine learning. PMLR, 2019, pp. 5301–5310

    2019

  22. [22]

    cnn-dp: Composite neural network with differential propagation for impulsive nonlinear dynamics,

    H. Lee, S. Han, H.-S. Choi, and J.-G. Kim, “cnn-dp: Composite neural network with differential propagation for impulsive nonlinear dynamics,” Journal of Computational Physics, vol. 496, p. 112578, 2024

    2024

  23. [23]

    Robust contact force estimation for robot manipulators in three-dimensional space,

    J. Jung, J. Lee, and K. Huh, “Robust contact force estimation for robot manipulators in three-dimensional space,”Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, vol. 220, no. 9, pp. 1317–1327, 2006

    2006

  24. [24]

    Mechanism-based structured deep neural network for cutting force forecasting using cnc inherent monitoring signals,

    Y . Cheng, Y . Li, X. Liu, and Y . Cai, “Mechanism-based structured deep neural network for cutting force forecasting using cnc inherent monitoring signals,”IEEE/ASME Transactions on Mechatronics, vol. 27, no. 4, pp. 2235–2245, 2021

    2021

  25. [25]

    Unsupervised domain adversarial adap- tive regression network for cutting force prediction at varying spindle speeds,

    C. Ni, J. Yang, and H. Ding, “Unsupervised domain adversarial adap- tive regression network for cutting force prediction at varying spindle speeds,”IEEE/ASME Transactions on Mechatronics, vol. 30, no. 1, pp. 252–263, 2024

    2024

  26. [26]

    On the theory of filter amplifiers,

    S. Butterworthet al., “On the theory of filter amplifiers,”Wireless Engineer, vol. 7, no. 6, pp. 536–541, 1930

    1930

  27. [27]

    Attention is all you need,

    A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,”Advances in neural information processing systems, vol. 30, 2017

    2017

  28. [28]

    Reversible instance normalization for accurate time-series forecasting against dis- tribution shift,

    T. Kim, J. Kim, Y . Tae, C. Park, J.-H. Choi, and J. Choo, “Reversible instance normalization for accurate time-series forecasting against dis- tribution shift,” inInternational conference on learning representations, 2021

    2021

  29. [29]

    Exploring a long short-term memory based encoder-decoder framework for multi-step- ahead flood forecasting,

    I.-F. Kao, Y . Zhou, L.-C. Chang, and F.-J. Chang, “Exploring a long short-term memory based encoder-decoder framework for multi-step- ahead flood forecasting,”Journal of Hydrology, vol. 583, p. 124631, 2020

    2020

  30. [30]

    Informer: Beyond efficient transformer for long sequence time-series forecasting,

    H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” inProceedings of the AAAI conference on artificial intel- ligence, vol. 35, no. 12, 2021, pp. 11 106–11 115

    2021

  31. [31]

    iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

    Y . Liu, T. Hu, H. Zhang, H. Wu, S. Wang, L. Ma, and M. Long, “itrans- former: Inverted transformers are effective for time series forecasting,” arXiv preprint arXiv:2310.06625, 2023

    work page internal anchor Pith review arXiv 2023

  32. [32]

    Adam: A Method for Stochastic Optimization

    D. P. Kingma, “Adam: A method for stochastic optimization,”arXiv preprint arXiv:1412.6980, 2014. Hyeonbeen Leereceived the B.Eng. and M.Eng. degrees in mechanical engineering from Kyung Hee University, Seoul, South Korea, in 2022 and 2024, respectively. He is currently an incoming Ph.D. student at Virginia Tech, Blacksburg, V A, USA. His research interest...

    work page internal anchor Pith review Pith/arXiv arXiv 2014