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arxiv: 2605.05461 · v1 · submitted 2026-05-06 · 💻 cs.RO

Contact-Free Grasp Stability Prediction with In-Hand Time-of-Flight Sensors

Kyle DuFrene , Cindy Grimm This is my paper

Pith reviewed 2026-05-08 16:02 UTC · model grok-4.3

classification 💻 cs.RO
keywords grasp stability predictiontime-of-flight sensorscontact-free graspingrobot manipulationin-hand sensingmachine learning classifiergripper design
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The pith

Time-of-flight sensors mounted in a gripper can predict grasp stability before any contact occurs.

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

The paper shows that distance readings from multi-zone time-of-flight sensors placed in the distal links of a robot gripper contain enough information to classify whether a planned grasp will hold after lifting. Earlier stability methods required closing the gripper and using tactile sensors afterward, which delays decisions and risks moving or dropping the object. By training a classifier on more than 2,500 real grasps across 15 objects, the approach reaches 85.5 percent accuracy on validation objects and 86 percent on three completely unseen test objects while producing predictions at 15 Hz. This removes the need to attempt a grasp simply to check its quality.

Core claim

The central claim is that pre-contact readings from multi-zone time-of-flight sensors embedded in a parallel-jaw gripper can be mapped by a trained classifier to post-grasp stability labels. Data collected from over 2,500 grasps on 15 objects produces a model with 85.5 percent accuracy on held-out validation objects and 86.0 percent accuracy on three additional unseen test objects. The resulting system runs at 15 Hz without requiring any physical contact or post-grasp tactile feedback.

What carries the argument

Multi-zone time-of-flight sensors that supply distance profiles before gripper closure, passed to a machine learning classifier trained to output stable or unstable labels.

If this is right

  • Grasp planners can discard unstable candidates before closing the gripper, shortening the overall planning cycle.
  • The 15 Hz rate allows the predictor to run continuously inside closed-loop control loops.
  • Performance on unseen objects implies the learned mapping can transfer to new items without retraining from scratch.
  • Removing the contact step lowers the chance of disturbing or damaging objects during failed grasp attempts.

Where Pith is reading between the lines

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

  • The same pre-contact sensor stream could be fused with overhead vision to reject poor grasps even earlier in cluttered scenes.
  • Extending the sensors to different gripper geometries might support stability prediction for underactuated or soft hands.
  • Large-scale simulation of time-of-flight readings could reduce the real-world data collection burden for new object sets.
  • The method highlights that geometric proximity information alone often suffices for stability decisions without full physics simulation.

Load-bearing premise

Pre-contact time-of-flight distance patterns are sufficient to determine whether the gripper will hold the object after lifting, for the range of objects and conditions encountered in deployment.

What would settle it

A measured drop in classification accuracy below 70 percent when the trained model is applied to grasps of objects whose shapes, sizes, weights, or surface properties lie outside the original training distribution.

Figures

Figures reproduced from arXiv: 2605.05461 by Cindy Grimm, Kyle DuFrene.

Figure 1
Figure 1. Figure 1: Left - The custom gripper mounted to the end of a Kinova Gen 3 arm. The soup can from the YCB object set is placed between the fingers. The location of the time-of-flight sensors is marked in blue and orange. A cutaway view of the distal link exposing the TOF sensor is shown below. Right - Top and side views from RVIZ showing the TOF sensor data. Orange points are from the left TOF sensor, blue from the ri… view at source ↗
Figure 2
Figure 2. Figure 2: A diagram of how our grasp stability classification would fit into existing grasping pipelines. Our approach does not require contact with the view at source ↗
Figure 3
Figure 3. Figure 3: The objects used within this paper. Left — Objects used for classifier training. Center — Objects used for classifier validation and selection. view at source ↗
Figure 4
Figure 4. Figure 4: ROC curve comparing unseen validation objects (used for model view at source ↗
Figure 5
Figure 5. Figure 5: A bar chart visualizing a confusion matrix on a per-object level (three unseen validation and test objects). The left column shows true positive view at source ↗
Figure 6
Figure 6. Figure 6: A trial with the sugar box which failed, but was incorrectly predicted view at source ↗
read the original abstract

Current approaches to grasp planning for robotics demonstrate high success rates, but degrade with noisy sensors and other factors. Previous works have proposed tactile-based grasp stability classifiers to detect failures, but these approaches rely on making contact and grasping the object to do so. We propose a contact-free grasp stability predictor using multi-zone time-of-flight sensors mounted in the distal links of a gripper. Our method, as it does not require grasping the object to make a prediction, significantly speeds up the stability classification process, cycling at 15 Hz. We collected over 2,500 real-world grasps across 15 objects to train a classifier. Additionally, we conducted grasp attempts over six additional unseen objects, three for validation and model selection, and three for model testing. Our approach demonstrated strong classification performance, with an accuracy of 85.5% on validation and 86.0% on test objects.

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

2 major / 2 minor

Summary. The manuscript proposes a contact-free grasp stability predictor for robotic grippers that uses multi-zone time-of-flight (ToF) sensors mounted on the distal links to classify grasp success from pre-contact distance readings. The approach is trained on over 2,500 real-world grasps collected across 15 objects and evaluated on six additional unseen objects (three for validation/model selection and three for final testing), achieving reported accuracies of 85.5% and 86.0% respectively while operating at 15 Hz without requiring physical contact.

Significance. If the generalization claims hold under expanded testing, the work provides a practical efficiency gain over contact-dependent tactile stability classifiers by enabling rapid pre-grasp assessment, which could reduce failed grasp attempts and hardware wear in manipulation pipelines. The real-world data collection and explicit separation of training/validation/test objects are positive elements. However, the narrow test distribution limits the assessed broader impact on robust robotic grasping in varied conditions.

major comments (2)
  1. [Abstract] Abstract and evaluation description: The headline 86.0% test accuracy rests on grasp attempts with only three unseen test objects after training on 15 objects and validation on three others. No per-object accuracy breakdown, grasp counts per test object, or characterization of object diversity (shape, mass, surface properties, center-of-mass) is provided. Since grasp stability depends on factors like friction and mass distribution that multi-zone ToF geometry readings capture only indirectly, the small test set raises the possibility that reported performance reflects object-specific correlations rather than a general pre-grasp predictor.
  2. [Methods] Methods and results sections: The manuscript provides insufficient detail on the classifier architecture, feature engineering from the multi-zone ToF readings, training procedure, hyperparameter selection, or any statistical significance testing of the accuracy figures. These omissions make it difficult to assess potential overfitting, data collection biases, or robustness, which are load-bearing for interpreting the 85.5%/86.0% claims as evidence of a reliable contact-free method.
minor comments (2)
  1. [Abstract] The abstract would be clearer if it stated the total number of grasp attempts performed on the validation and test objects.
  2. Consider adding a table or figure showing example ToF sensor readings for successful vs. failed grasps to illustrate the input features.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We have revised the paper to address the concerns about evaluation details and methods transparency. Our responses to the major comments are provided below.

read point-by-point responses

  1. Referee: [Abstract] Abstract and evaluation description: The headline 86.0% test accuracy rests on grasp attempts with only three unseen test objects after training on 15 objects and validation on three others. No per-object accuracy breakdown, grasp counts per test object, or characterization of object diversity (shape, mass, surface properties, center-of-mass) is provided. Since grasp stability depends on factors like friction and mass distribution that multi-zone ToF geometry readings capture only indirectly, the small test set raises the possibility that reported performance reflects object-specific correlations rather than a general pre-grasp predictor.

    Authors: We agree that additional characterization of the test set is warranted to support the generalization claims. In the revised manuscript, we have added a per-object performance table reporting accuracy and grasp counts for each of the three test objects, along with a description of their diversity in terms of shape categories, approximate masses, surface textures, and center-of-mass estimates. The test objects were deliberately selected to differ from the training distribution in geometry and material properties. While the limited number of test objects is a constraint of the current study, the explicit train/validation/test object separation and comparable accuracies (85.5% validation, 86.0% test) provide evidence against purely object-specific correlations. We have also expanded the discussion section to acknowledge the narrow test distribution as a limitation and outline plans for broader evaluation. revision: yes

  2. Referee: [Methods] Methods and results sections: The manuscript provides insufficient detail on the classifier architecture, feature engineering from the multi-zone ToF readings, training procedure, hyperparameter selection, or any statistical significance testing of the accuracy figures. These omissions make it difficult to assess potential overfitting, data collection biases, or robustness, which are load-bearing for interpreting the 85.5%/86.0% claims as evidence of a reliable contact-free method.

    Authors: We acknowledge the need for greater methodological transparency. The revised Methods section now includes: (1) the full classifier architecture and input feature vector (per-zone distance statistics including min, max, mean, and variance across the multi-zone ToF sensors); (2) the training procedure, including how grasps were labeled as stable/unstable and the use of object-wise cross-validation to avoid leakage; (3) hyperparameter selection via grid search with the validation set; and (4) statistical significance testing of the accuracy figures against a majority-class baseline using McNemar's test. These additions enable readers to evaluate overfitting risks and robustness more rigorously. revision: yes

Circularity Check

0 steps flagged

No significant circularity; standard ML train/test split on held-out objects

full rationale

The paper trains a classifier on >2500 grasps from 15 objects, performs model selection on 3 separate validation objects, and reports accuracy on 3 further unseen test objects. This is a conventional supervised learning pipeline with explicit held-out data; the reported 86.0% test accuracy is not equivalent to any training input by construction. No equations, self-definitional steps, fitted-input-as-prediction, or load-bearing self-citations appear in the abstract or described methodology. The derivation chain is self-contained and externally falsifiable via the separate test objects.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on empirical training of a supervised classifier on real grasp data, with no new physical laws or entities postulated.

free parameters (1)
  • ML model parameters
    The classifier weights and hyperparameters are fitted to the over 2500 real-world grasps data.
axioms (1)
  • domain assumption The sensor readings are independent and identically distributed with the training data
    Standard assumption for ML generalization to test objects.

pith-pipeline@v0.9.0 · 5448 in / 1189 out tokens · 53448 ms · 2026-05-08T16:02:14.489561+00:00 · methodology

discussion (0)

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