Pith. sign in

REVIEW 3 major objections 26 references

SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots

T0 review · 3 major / 0 minor · reviewed 2026-06-26 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A multimodal sensorized foot paired with an LSTM model detects early slips in quadruped robots at an average 24mm displacement.

desk verdict SlipSense shows a workable custom multimodal foot plus LSTM for force-based slip detection on the Go1 with concrete numbers, but the ground-truth method for the 24mm displacement claim is missing, so the 3.3x improvement over kinematics cannot be verified from the abstract. read the letter →

arxiv 2606.24350 v1 pith:UW2ARSVI submitted 2026-06-23 cs.RO

Iris Szu-Yao Liu , Chien Chern Cheah , Meng Yee Michael Chuah This is my paper · ORCID
classification cs.RO
keywords slipdetectionleggedrobotsmultimodalsensingLSTMgroundreactionforcesquadrupedonlinesensorizedfoot
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper presents SlipSense as a framework that mounts custom multimodal sensors on a quadruped foot to measure forces in real time and feed them to an LSTM model. The model infers ground reaction forces and flags slip anomalies during locomotion, achieving detection of slips as small as 24.1mm on average with 85.9 percent accuracy on a Unitree Go1 robot. This matters because kinematic methods that rely on foot velocity from state estimation miss these early slips and allow instability to build. By catching slips sooner the system supports force-aware gait changes that keep the robot balanced on slippery terrain. The reported results show a 3.3-fold improvement in resolution and 24 percent higher accuracy over the kinematic baseline.

What carries the argument

The multimodal sensorized foot that collects real-time data to feed into an LSTM model for inferring ground reaction forces and identifying slip anomalies.

What would settle it

An experiment on the same robot over a new slippery surface where minimum detectable displacement exceeds 50mm or accuracy falls below 70 percent would falsify the claimed performance.

Watch

Extended reading notes

Core claim

SlipSense integrates a multimodal sensor design with a LSTM-based model to infer ground reaction forces and detect slip-indicative anomalies during locomotion on a Unitree Go1 quadruped, achieving detection of early-stage slips at 24.1 +/-6.4mm average displacement with 85.9% accuracy, which is 3.3 times finer and 24% more accurate than a kinematic baseline.

Load-bearing premise

The multimodal sensor design and LSTM model can reliably infer ground reaction forces and identify slip-indicative anomalies from real-time data collected during locomotion on the Unitree Go1.

Editorial extensions

If this is right

  • Future controllers can use the detected forces to estimate terrain friction coefficients in real time.
  • Gait planners can adjust step constraints dynamically to prevent escalation from early slip to full instability.
  • The same sensor suite supports blind operation without external vision or motion capture.
  • Overall locomotion stability improves on variable-friction surfaces through proactive rather than reactive responses.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The sensor foot could be scaled to other legged platforms if the LSTM is retrained on their dynamics.
  • Adding the slip signal to existing state estimators might reduce reliance on expensive motion-capture validation.
  • The framework opens a path to online friction mapping that could feed into model-predictive controllers.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 0 minor

Summary. The paper introduces SlipSense, a framework for online slip detection in legged robots. It uses a custom lightweight multimodal sensorized foot on a quadruped combined with an LSTM model to infer ground reaction forces and detect slip-indicative anomalies in real time. The work reports hardware deployment on a Unitree Go1, claiming detection of early-stage slips at an average displacement of 24.1 +/- 6.4 mm with 85.9% overall accuracy. This is presented as a 3.3-fold finer resolution and 24% relative accuracy improvement over a standard kinematic baseline relying on state-estimation foot velocity. The framework is positioned as a foundation for force-aware gait adaptation and terrain friction estimation.

Significance. If the experimental claims hold after proper validation, the result would provide a practical hardware-software approach to earlier slip detection than kinematics alone, with direct applicability to improving stability on variable terrains. The reported numbers, if independently verifiable, would constitute a concrete, falsifiable performance benchmark for force-based sensing in commercial quadrupeds.

major comments (3)
  1. [Abstract] Abstract: the central performance claims (average slip displacement of 24.1 +/-6.4 mm at detection, 85.9% accuracy, 3.3-fold resolution improvement) are stated without any description of the ground-truth measurement method used to quantify actual foot displacement at the instant the LSTM flags a slip. No mention is made of motion capture, external encoders, or an independent reference system, so the resolution and accuracy numbers cannot be verified.
  2. [Abstract] Abstract: the comparison to the kinematic baseline is load-bearing for the claimed improvement, yet the manuscript supplies no information on how slip events were labeled or whether the ground-truth displacement shares the same state-estimation pipeline used by the baseline. If labeling depends on the baseline estimator, the reported 3.3-fold gain is circular by construction.
  3. [Abstract] Abstract: no details are provided on sensor calibration procedures, data collection protocol (e.g., number of trials, terrain types, locomotion speeds), LSTM architecture/training procedure, validation splits, or statistical testing. These omissions make it impossible to assess whether the reported accuracy and displacement figures are supported by the collected data.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful review and for identifying the lack of supporting methodological information in the abstract. We agree that the abstract, in its current form, does not supply enough detail for independent verification of the reported performance metrics. We will revise the abstract to incorporate concise descriptions of the ground-truth method, labeling procedure, and experimental protocol while preserving its length constraints.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central performance claims (average slip displacement of 24.1 +/-6.4 mm at detection, 85.9% accuracy, 3.3-fold resolution improvement) are stated without any description of the ground-truth measurement method used to quantify actual foot displacement at the instant the LSTM flags a slip. No mention is made of motion capture, external encoders, or an independent reference system, so the resolution and accuracy numbers cannot be verified.

    Authors: We agree that the abstract omits any description of the ground-truth measurement method. This information will be added to the revised abstract. revision: yes

  2. Referee: [Abstract] Abstract: the comparison to the kinematic baseline is load-bearing for the claimed improvement, yet the manuscript supplies no information on how slip events were labeled or whether the ground-truth displacement shares the same state-estimation pipeline used by the baseline. If labeling depends on the baseline estimator, the reported 3.3-fold gain is circular by construction.

    Authors: We agree that the abstract provides no information on slip-event labeling or its relationship to the kinematic baseline. We will revise the abstract to state that labeling was performed with an independent reference system distinct from the baseline's state estimator. revision: yes

  3. Referee: [Abstract] Abstract: no details are provided on sensor calibration procedures, data collection protocol (e.g., number of trials, terrain types, locomotion speeds), LSTM architecture/training procedure, validation splits, or statistical testing. These omissions make it impossible to assess whether the reported accuracy and displacement figures are supported by the collected data.

    Authors: We agree that the abstract contains none of the requested experimental details. We will add a short summary of the sensor calibration, data-collection protocol, model architecture, and validation approach to the revised abstract. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity; experimental performance metrics from hardware tests

full rationale

The paper reports results from physical deployment of a multimodal foot sensor and LSTM model on the Unitree Go1 quadruped, claiming measured slip detection resolution and accuracy against a kinematic baseline. No derivation chain, equations, fitted parameters renamed as predictions, or self-citations appear in the provided text. The claims rest on direct experimental outcomes rather than any self-referential construction or reduction to inputs by definition. This matches the default case of a self-contained experimental robotics paper.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Abstract-only review prevents exhaustive enumeration; the central claim rests on the domain assumption that multimodal foot sensors yield data from which ground reaction forces and slip anomalies can be inferred by the LSTM.

assumptions (1)
  • domain assumption Multimodal sensor data from the custom foot can be used to infer ground reaction forces and detect slip-indicative anomalies during locomotion
    Invoked when describing the framework integration and online detection capability.

how reviews work

0 comments
Cite this review

Pith. "Pith review of SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots." pith.science (2026). https://pith.science/paper/UW2ARSVI

@misc{pith2026260624350,
  author       = {Pith},
  title        = {Pith review of: SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UW2ARSVI}},
  note         = {Machine review of arXiv:2606.24350}
}
read the original abstract

Legged robots rely on accurate ground interaction awareness to traverse variable terrains, such as slippery surfaces. Existing slip detection methods often rely on kinematics and proprioception, which lack the sensitivity to detect early-stage slips that occur prior to catastrophic instability. Thus, this paper presents SlipSense, a novel framework for online force-based slip detection using a custom lightweight sensorized foot for quadrupeds to detect slip. The framework integrates a multimodal sensor design with a LSTM-based model to infer ground reaction forces and detect slip-indicative anomalies during locomotion. The proposed framework is deployed on a Unitree Go1 quadruped to demonstrate blind online slip detection over a slippery terrain. Our method detects early-stage slips down to an average displacement of 24.1 +/-6.4mm with an overall accuracy of 85.9%. This represents a 3.3-fold finer detection resolution and a 24% relative accuracy improvement over a standard kinematic baseline that uses foot velocity inferred through state estimation. The work in this paper serves as a foundation for force-aware gait adaptation in legged robotic locomotion, allowing future controllers to estimate terrain friction and adjust constraints, thus improving the overall stability of the system.

Figures

Figures reproduced from arXiv: 2606.24350 by the authors.

Figure 1
Figure 1. SlipSense: multimodal online slip detection for legged robots using [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the proposed SlipSense framework for online slip detection in quadruped robots. The system consists of three modules: (1) a lightweight [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Our custom multimodal footpad sensor mounted onto a Unitree [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Comparison of simultaneous tangential and normal force predictions [PITH_FULL_IMAGE:figures/full_fig_p004_4.png]
Figure 5
Figure 5. Figure 5: The proposed slip detection framework, illustrating the offline training pipeline and online deployment. This framework consists of three key modules: [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 6
Figure 6. Figure 6: Experimental setup in motion capture lab with force plates. The setup [PITH_FULL_IMAGE:figures/full_fig_p005_6.png]
Figure 7
Figure 7. Figure 7: Data-driven ground truth labeling and visualization of different slips. (a) distributions of foot position deviations, [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 8
Figure 8. Figure 8: Experimental results comparison between baseline and the proposed multimodal SlipSense slip detection models. Shown are 100 stances and the [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Box plot showing the smallest 30 slip stances accurately classified [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

26 extracted references · 1 canonical work pages

  1. [1]

    Contact model fusion for event-based locomotion in unstructured ter- rains

    Gerardo Bledt, Patrick M Wensing, Sam Ingersoll, and Sangbae Kim. Contact model fusion for event-based locomotion in unstructured ter- rains. In2018 IEEE International Conference on Robotics and Automa- tion (ICRA), pages 4399–4406. IEEE, 2018

    2018

  2. [2]

    The MIT super mini cheetah: A small, low-cost quadrupedal robot for dynamic locomotion

    Will Bosworth, Sangbae Kim, and Neville Hogan. The MIT super mini cheetah: A small, low-cost quadrupedal robot for dynamic locomotion. In2015 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), pages 1–8. IEEE, 2015

    2015

  3. [3]

    Slip detection and recovery for quadruped robots

    Michele Focchi, Victor Barasuol, Marco Frigerio, Darwin G Caldwell, and Claudio Semini. Slip detection and recovery for quadruped robots. Robotics Research: V olume 2, pages 185–199, 2017

    2017

  4. [4]

    Slip detec- tion and recovery for quadruped robots via orthogonal decomposition

    Chengzhen Yan, Jiahu Qin, Qingchen Liu, and Qichao Ma. Slip detec- tion and recovery for quadruped robots via orthogonal decomposition. IEEE Transactions on Industrial Electronics, 2024

    2024

  5. [5]

    Robust rough-terrain locomotion with a quadrupedal robot

    P ´eter Fankhauser, Marko Bjelonic, C Dario Bellicoso, Takahiro Miki, and Marco Hutter. Robust rough-terrain locomotion with a quadrupedal robot. In2018 IEEE International Conference on Robotics and Automa- tion (ICRA), pages 5761–5768. IEEE, 2018

    2018

  6. [6]

    Extended active observer for force estimation and disturbance rejection of robotic manip- ulators.Robotics and Autonomous Systems, 61(12):1277–1287, 2013

    Linping Chan, Fazel Naghdy, and David Stirling. Extended active observer for force estimation and disturbance rejection of robotic manip- ulators.Robotics and Autonomous Systems, 61(12):1277–1287, 2013

    2013

  7. [7]

    Contact force estimation method of legged-robot and its application in impedance control.IEEE Access, 8:161175–161187, 2020

    Zhang Cong, An Honglei, Chengye Wu, Lin Lang, Qing Wei, and Ma Hongxu. Contact force estimation method of legged-robot and its application in impedance control.IEEE Access, 8:161175–161187, 2020

    2020

  8. [8]

    Walking on TacTip toes: A tactile sensing foot for walking robots

    Elizabeth A Stone, Nathan F Lepora, and David AW Barton. Walking on TacTip toes: A tactile sensing foot for walking robots. In2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 9869–9875. IEEE, 2020

    2020

Show all 26 references
  1. [9]

    Bi-modal hemispherical sensors for dynamic locomotion and manipulation

    Lindsay Epstein, Andrew SaLoutos, Donghyun Kim, and Sangbae Kim. Bi-modal hemispherical sensors for dynamic locomotion and manipulation. In2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 7381–7381. IEEE, 2020

    2020

  2. [10]

    Foottile: a rugged foot sensor for force and center of pressure sensing in soft terrain

    Felix Ruppert and Alexander Badri-Spr ¨owitz. Foottile: a rugged foot sensor for force and center of pressure sensing in soft terrain. In2020 IEEE International Conference on Robotics and Automation (ICRA), pages 4810–4816. IEEE, 2020

    2020

  3. [11]

    Integrated ground reaction force sensing and terrain classifi- cation for small legged robots.IEEE Robotics and Automation Letters, 1(2):1125–1132, 2016

    X Alice Wu, Tae Myung Huh, Rudranarayan Mukherjee, and Mark Cutkosky. Integrated ground reaction force sensing and terrain classifi- cation for small legged robots.IEEE Robotics and Automation Letters, 1(2):1125–1132, 2016

    2016

  4. [12]

    Tactile sensing and terrain-based gait control for small legged robots.IEEE Transactions on Robotics, 36(1):15–27, 2019

    X Alice Wu, Tae Myung Huh, Aaron Sabin, Srinivasan A Suresh, and Mark R Cutkosky. Tactile sensing and terrain-based gait control for small legged robots.IEEE Transactions on Robotics, 36(1):15–27, 2019

    2019

  5. [13]

    Tactid: High-performance visuo-tactile sensor-based terrain identification for legged robots.IEEE Sensors Journal, 2024

    Ziwu Song, Chenchang Li, Zhentan Quan, Shilong Mu, Xiaosa Li, Ziyi Zhao, Wanxin Jin, Chenye Wu, Wenbo Ding, and Xiao-Ping Zhang. Tactid: High-performance visuo-tactile sensor-based terrain identification for legged robots.IEEE Sensors Journal, 2024

    2024

  6. [14]

    Quadrupedal locomotion on uneven terrain with sensorized feet.IEEE Robotics and Automation Letters, 5(2):1548–1555, 2020

    Giorgio Valsecchi, Ruben Grandia, and Marco Hutter. Quadrupedal locomotion on uneven terrain with sensorized feet.IEEE Robotics and Automation Letters, 5(2):1548–1555, 2020

    2020

  7. [15]

    Soft foot sensor design and terrain classification for dynamic legged locomotion

    Xiaofeng Guo, Bryan Blaise, Jennifer Molnar, Jeremiah Coholich, Shantanu Padte, Ye Zhao, and Frank L Hammond. Soft foot sensor design and terrain classification for dynamic legged locomotion. In 2020 3rd IEEE International Conference on Soft Robotics (RoboSoft), pages 550–557....

    2020

  8. [16]

    Evaluation of ground slipperiness during collision using MEMS local slip sensor

    Taiyu Okatani and Isao Shimoyama. Evaluation of ground slipperiness during collision using MEMS local slip sensor. In2019 IEEE 32nd In- ternational Conference on Micro Electro Mechanical Systems (MEMS), pages 823–825. IEEE, 2019

    2019

  9. [17]

    Design of anti-skid foot with passive slip detection mechanism for conditional utilization of heterogeneous foot pads.IEEE Robotics and Automation Letters, 4(2):1170–1177, 2019

    Jaejun Park and Hae-Won Park. Design of anti-skid foot with passive slip detection mechanism for conditional utilization of heterogeneous foot pads.IEEE Robotics and Automation Letters, 4(2):1170–1177, 2019

    2019

  10. [18]

    Integrated barometric pressure sensors on legged robots for enhanced tactile exploration of edges.IEEE Robotics and Automation Letters, 2024

    Thijs Van Hauwermeiren, Anatolii Sianov, Annelies Coene, and Guil- laume Crevecoeur. Integrated barometric pressure sensors on legged robots for enhanced tactile exploration of edges.IEEE Robotics and Automation Letters, 2024

    2024

  11. [19]

    Walking on partial footholds including line contacts with the humanoid robot atlas

    Georg Wiedebach, Sylvain Bertrand, Tingfan Wu, Luca Fiorio, Stephen McCrory, Robert Griffin, Francesco Nori, and Jerry Pratt. Walking on partial footholds including line contacts with the humanoid robot atlas. In2016 IEEE-RAS 16th International Conference on Humanoid Robots (H...

    2016

  12. [20]

    On slip detection for quadruped robots.Sensors, 22(8):2967, 2022

    Ylenia Nistic `o, Shamel Fahmi, Lucia Pallottino, Claudio Semini, and Geoff Fink. On slip detection for quadruped robots.Sensors, 22(8):2967, 2022

    2022

  13. [21]

    State estimation for legged robots on unstable and slippery terrain

    Michael Bloesch, Christian Gehring, P ´eter Fankhauser, Marco Hutter, Mark A Hoepflinger, and Roland Siegwart. State estimation for legged robots on unstable and slippery terrain. In2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 6058–6064. IEEE, 2013

    2013

  14. [22]

    Legged robot state estimation in slippery environments using invariant extended kalman filter with velocity update

    Sangli Teng, Mark Wilfried Mueller, and Koushil Sreenath. Legged robot state estimation in slippery environments using invariant extended kalman filter with velocity update. In2021 IEEE International Con- ference on Robotics and Automation (ICRA), pages 3104–3110. IEEE, 2021

    2021

  15. [23]

    Probabilistic contact state estima- tion for legged robots using inertial information.arXiv preprint arXiv:2303.00538, 2023

    Michael Maravgakis, Despina-Ekaterini Argiropoulos, Stylianos Piper- akis, and Panos Trahanias. Probabilistic contact state estima- tion for legged robots using inertial information.arXiv preprint arXiv:2303.00538, 2023

    2023

  16. [24]

    Learning slip detection for agile locomotion of quadruped robots

    Peng Sun, Junjie Qiang, Letian Qian, and Xin Luo. Learning slip detection for agile locomotion of quadruped robots. In2023 IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 1–6. IEEE, 2023

    2023

  17. [25]

    Bi-modal hemispherical sensor: A unifying solution for three axis force and contact angle measurement

    Meng Yee Michael Chuah, Lindsay Epstein, Donghyun Kim, Juan Romero, and Sangbae Kim. Bi-modal hemispherical sensor: A unifying solution for three axis force and contact angle measurement. In2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages ...

    2019

  18. [26]

    Enabling force sens- ing during ground locomotion: A bio-inspired, multi-axis, composite force sensor using discrete pressure mapping.IEEE Sensors Journal, 14(5):1693–1703, 2014

    Meng Yee Michael Chuah and Sangbae Kim. Enabling force sens- ing during ground locomotion: A bio-inspired, multi-axis, composite force sensor using discrete pressure mapping.IEEE Sensors Journal, 14(5):1693–1703, 2014

    2014

Pith tools

Reviewed June 26, 2026 · model on record in the stance chip above.