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arxiv: 2606.24350 · v1 · pith:UW2ARSVInew · submitted 2026-06-23 · 💻 cs.RO

SlipSense: Multimodal Sensing for Online Slip Detection in Legged Robots

Iris Szu-Yao Liu , Chien Chern Cheah , Meng Yee Michael Chuah This is my paper

Pith reviewed 2026-06-26 00:14 UTC · model grok-4.3

classification 💻 cs.RO
keywords slip detectionlegged robotsmultimodal sensingLSTMground reaction forcesquadrupedonline detectionsensorized foot
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The pith

A multimodal sensorized foot paired with an LSTM model detects early slips in quadruped robots at an average 24mm displacement.

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

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.

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.

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.

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.

Where Pith is reading between the lines

These are 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.

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.

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.

Figures

Figures reproduced from arXiv: 2606.24350 by Chien Chern Cheah, Iris Szu-Yao Liu, Meng Yee Michael Chuah.

Figure 1
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. 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. Figure 3: Our custom multimodal footpad sensor mounted onto a Unitree [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of simultaneous tangential and normal force predictions [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
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] view at source ↗
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] view at source ↗
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] view at source ↗
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] view at source ↗
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] view at source ↗
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.

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 / 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

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.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 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.

axioms (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.

pith-pipeline@v0.9.1-grok · 5743 in / 1250 out tokens · 37559 ms · 2026-06-26T00:14:07.938506+00:00 · methodology

discussion (0)

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