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REVIEW 4 major objections 4 minor 33 references

Measuring Uncertainty in Shape Completion to Improve Grasp Quality

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Adding shape-completion uncertainty to grasp scores raises real top-5 grasp success to 58 percent.

desk verdict The plug-in uncertainty penalty is a sensible idea, but the headline +7 p.p. claim rests on a comparison that changes two variables at once and the evidence as presented does not isolate the uncertainty signal. read the letter →

arxiv 2504.16183 v1 pith:47C3YCPD submitted 2025-04-22 cs.RO

Nuno Ferreira Duarte , Seyed S. Mohammadi , Plinio Moreno , Alessio Del Bue , Jose Santos-Victor This is my paper · ORCID
classification cs.RO
keywords uncertaintyestimationMonteCarlodropoutshapecompletiongraspqualitypointcloudrobotgraspingsingle-viewperceptionre-ranking
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 tries to establish that a robot grasps partially seen objects more reliably when the grasp planner is told how unsure the shape-completion network is about the region it will touch. It estimates that uncertainty by leaving dropout on during inference, running the completion network 60 times on a single-view point cloud, and taking the per-point standard deviation of the completed shape. Each grasp candidate's quality score is then reduced by a weighted sum of the standard deviation inside the gripper's contact volume, and the candidates are re-ranked. On a real seven-degree-of-freedom arm with a two-finger gripper and ten household objects, the top five re-ranked grasps succeeded 58 percent of the time, against 51 percent for the same pipeline without uncertainty and 35 percent for the grasp generator alone. The paper's message is that uncertainty in reconstructed geometry is usable signal for grasp selection.

What carries the argument

The machinery is a score update of the form $S' = S - W_u \sum_{p\in CP'} \sigma_u(p)$, applied to each of the fifteen grasp candidates: crop the completed point cloud to the bounding box defined by the gripper's contact points, sum the per-point standard deviations inside that crop, and subtract the scaled sum from the original grasp score. The standard deviations come from Monte Carlo dropout, with dropout left active at inference and $T=60$ forward passes averaged into the completed cloud and a variance map. The weight $W_u$ is calibrated to the score scale of the grasp detector ($10^5$ for the GPD-style scores, $10^{-1}$ for the PointNetGPD-style scores) so the uncertainty term has comparable influence across metrics. Re-ordering by $S'$ is what moves low-uncertainty, visible-side grasps into the top five.

What would settle it

Have each baseline rank its top five from the same fifteen candidates the proposed method uses; if a baseline then matches the 58 percent success, the uncertainty term contributed nothing. Also run the proposed pipeline with dropout switched off at inference, and if the re-ranking still helps, the reported standard deviations are not the cause.

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Extended reading notes

Core claim

The central claim is that a penalisation term built from the local standard deviation of a completed point cloud re-ranks grasp candidates better than the original quality score alone. The method does not modify the grasp generator or the shape-completion network; it keeps dropout active at inference, averages 60 forward passes into a completed cloud, and computes the per-point standard deviation of those passes. For each candidate, the completed points inside the gripper's contact bounding box are cropped, their standard deviations summed, and that sum, scaled by a weight, is subtracted from the grasp score. Because the highest uncertainty lies on the side of the object farthest from the observed view, the re-ranking systematically favours grasps on the visible, reachable side. In the real-robot trials the re-ranked top five averaged 58 percent success, seven percentage points above the same pipeline without the uncertainty term and twenty-three points above the grasp generator on the partial view alone.

Load-bearing premise

The result depends on the experimental comparison being fair: the proposed pipeline re-ranks fifteen generated candidates and keeps five, while the baselines take five directly from their original scores, and the uncertainty estimate only means something if the pre-trained completion model actually used dropout during training.

Editorial extensions

If this is right

  • Any off-the-shelf point-cloud grasp detector can be re-ranked with this uncertainty term without retraining or changing the set of generated candidates.
  • Grasps on the observed, near side of the object are systematically preferred, because points visible to the camera carry near-zero standard deviation, which also tends to avoid unreachable grasps.
  • The added cost is modest: roughly four seconds for the completed cloud plus uncertainty and two seconds for re-ranking, keeping the pipeline usable online.
  • The same uncertainty penalty transfers to a second grasp generator and improves its top-five success from 32 to 49 percent on average, suggesting the gain is not tied to one detector.
  • Precision at the top of the ranking becomes a better predictor of execution success, which is the quantity a robot actually acts on.

Reading between the lines

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

  • If the mechanism is the penalty on high-variance reconstructed regions, then any calibrated per-point uncertainty source, such as ensemble disagreement or learned variance, should produce a similar re-ranking; this is testable without changing the rest of the pipeline.
  • The reported comparison is not fully controlled on candidate-pool size, since the proposed method draws its top five from fifteen candidates while the baselines take five directly from their original scores; a baseline that also selects five from fifteen using the original score would isolate the uncertainty contribution.
  • The same score-update idea could be applied to other grippers by replacing the contact bounding box with the corresponding contact region, and to suction grasps by penalising the uncertainty under the suction cup.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper proposes to quantify the uncertainty of a point-cloud shape-completion network (3DSGrasp) at inference using Monte Carlo dropout with T=60 forward passes, producing both an averaged completed point cloud and a per-point standard deviation. This uncertainty is incorporated into the GPD grasp quality score by subtracting a weighted sum of standard deviations over the points inside each grasp candidate's bounding box (Eq. (2)), after which the top 15 grasp candidates are re-ranked and the new top 5 are executed. Real-robot experiments on 10 YCB objects with a Kinova Gen3 and Robotiq 2F-85 compare top-5 grasp success against GPD, GPD+3DSGrasp, PointNetGPD, and PointNetGPD+3DSGrasp. The paper reports average success rates of 58% for GPD+ours versus 51% for GPD+3DSGrasp and 35% for GPD, and concludes that accounting for completion uncertainty improves grasp ranking.

Significance. If the causal claim were established, the contribution would be practically useful: a plug-in uncertainty penalty that can be added to any point-cloud grasp scorer without retraining, validated on a real robot with a substantial number of trials (3000 grasp attempts across all methods). The paper also evaluates two grasp generators (GPD and PointNetGPD), which gives useful breadth, and it builds on a peer-reviewed backbone (3DSGrasp) rather than introducing a new architecture. However, as detailed below, the headline comparison is not yet controlled for candidate-pool size or for the effect of re-ranking per se, and the uncertainty signal rests on an unverified property of the pretrained network. The manuscript contains the ingredients of a useful empirical study, but the current evidence does not isolate the uncertainty term as the cause of the reported improvement.

major comments (4)
  1. [Section IV / Table I] The headline comparison is confounded by asymmetric candidate generation. The proposed pipeline explicitly generates 15 GPD candidates and then re-ranks them ('we made the decision of generating the rank 15 grasp candidates'), while the GPD and GPD+3DSGrasp baselines execute the top 5 of the original GPD ranking. The experiment therefore varies two factors at once: the size of the candidate pool (15 versus 5) and the uncertainty-based re-scoring. No condition applies the same 15-to-5 selection with the original GPD score, with a random perturbation, or with a non-uncertainty heuristic such as penalizing distance from the camera. Without such a control, the +7 p.p. improvement over GPD+3DSGrasp cannot be attributed to the uncertainty signal. This concern is reinforced by Section VI, which states that the proposed score 'favours the visible regions of the object that is also the side that is closest to the robot'; any heuristic that prefers visible, near-side points could reproduce the same effect.
  2. [Table I / Section V] The reported success rates are point estimates without confidence intervals, significance tests, or per-trial variance. With 50 grasp attempts per object per method, the difference between 58% and 51% on the average could easily arise from sampling noise, especially because object-level success rates vary widely (e.g., 2% versus 28% for 'Pitcher with lid'). The paper should report binomial confidence intervals or a paired comparison across trials, and should identify which pairwise differences are statistically reliable.
  3. [Equation (2) / Section IV] The uncertainty weight Wu is hand-tuned (10^5 for GPD and 10^-1 for PointNetGPD) and no sensitivity analysis is provided. Because the penalty term scales linearly with Wu and the GPD scores are in hundreds or thousands, the final ranking can be dominated by the uncertainty term at the chosen scale. The paper notes that two objects (mustard and jell-o) did not improve and attributes this to a 'balancing issue,' but it does not investigate how the result depends on Wu. A sweep over Wu, or an automatic principled calibration, is necessary to show that the reported ranking is not an artifact of a single favorable weight.
  4. [Section III-B / III-C] The Monte Carlo dropout uncertainty estimate is only valid if the deployed 3DSGrasp network actually contains dropout layers that were active during training and can be re-enabled at inference. The paper does not report the network architecture's dropout placement, the dropout rate, or any check that the T=60 forward passes produce nonzero variance. If the released pretrained model has no dropout in the relevant layers, the reported standard deviations would be zero or constant, and Equation (2) would reduce to the original score. The authors should verify and document this property, for example by showing the distribution of predicted standard deviations over passes and comparing with a network with dropout disabled.
minor comments (4)
  1. [Section IV / Algorithm 1] There are typographical errors: 'It is important to not that' should read 'note that', and 'BoudingBox' should be 'BoundingBox'.
  2. [Section V / VI] The heading sequence is inconsistent: both Sections V and VI are titled 'Robot Experiments'; the second one should probably be 'Discussion' or 'Additional Experiments'.
  3. [Table II] Table II is described as an 'ablation study', but it actually compares against results reported in other papers under different experimental protocols. This should be relabeled as a comparison to published methods, and the assumptions (rank-1 execution, different object sets, and different robot setups) should be stated in the table caption.
  4. [Section V] Please clarify whether the robot re-grasps the same object placement for every method's top-5 candidates (a paired design) or whether each method is evaluated in separate placements; the sentence 'in each trial we execute the rank 5 grasp candidates for all methods' is ambiguous.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the uncertainty re-ranking is an external additive score tested on real-robot outcomes, and the self-cited 3DSGrasp backbone is a fixed baseline rather than a forced constraint.

full rationale

The paper's central claim is that adding a shape-completion uncertainty term to a grasp quality score improves rank-5 grasping success. The uncertainty term in Equation (2) is an additive penalty computed from the standard deviation of Monte Carlo dropout predictions across T=60 forward passes, a technique attributed to Gal and Ghahramani [31], which is external to the authors. The success rates in Table I come from real-robot grasping trials on YCB objects, not from a derivation that assumes those success rates as input. The self-citation to 3DSGrasp [4] is present, and 3DSGrasp is used as the shape-completion backbone, but it is a fixed, pretrained, peer-reviewed baseline; the paper does not invoke any uniqueness theorem or forced mathematical conclusion from the authors' prior work. The uncertainty weight Wu is hand-set rather than fitted to Table I, and the paper acknowledges objects where the method did not improve, which is inconsistent with the weight having been optimized against the reported outcomes. The strongest weakness is experimental rather than circular: the proposed pipeline generates 15 grasp candidates and then re-ranks them, while the baselines execute the top 5 directly from the original score, so the comparison does not isolate the effect of the uncertainty re-ranking from the larger candidate pool. That is a control or comparability concern, not a case of the prediction being equivalent to the input by construction. Therefore no circular step is identified; the score reflects only the minor, non-load-bearing self-citation of the backbone.

Assumptions & free parameters 1 free parameters · 2 assumptions · 0 invented entities

The central claim depends on one hand-tuned weight and on two unverified assumptions about Monte Carlo dropout and the pretrained model. There are no invented physical or conceptual entities.

free parameters (1)
  • Wu (uncertainty weight) = 10^5 for GPD, 10^-1 for PointNetGPD
    Hand-tuned to balance the scale of GPD/PointNetGPD scores against the summed standard deviation. No sensitivity analysis or held-out selection is reported, and the choice directly affects the re-ranking and the reported success rates.
assumptions (2)
  • domain assumption Monte Carlo dropout with T forward passes approximates the posterior predictive distribution of the network.
    Used in Section III-B as the basis for estimating per-point standard deviation. The approximation from Gal and Ghahramani [31] is standard but requires the network to have been trained with dropout.
  • ad hoc to paper The pretrained 3DSGrasp network contains dropout layers that were active during training, so that retaining dropout at inference produces meaningful stochasticity.
    Section III-C and Section V state that the pretrained model from [4] is used with Monte Carlo dropout, but the paper does not verify that this model was trained with dropout or that the variance is calibrated. If this assumption fails, the uncertainty values are not a Bayesian estimate.

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Pith. "Pith review of Measuring Uncertainty in Shape Completion to Improve Grasp Quality." pith.science (2026). https://pith.science/paper/47C3YCPD

@misc{pith2026250416183,
  author       = {Pith},
  title        = {Pith review of: Measuring Uncertainty in Shape Completion to Improve Grasp Quality},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/47C3YCPD}},
  note         = {Machine review of arXiv:2504.16183}
}
read the original abstract

Shape completion networks have been used recently in real-world robotic experiments to complete the missing/hidden information in environments where objects are only observed in one or few instances where self-occlusions are bound to occur. Nowadays, most approaches rely on deep neural networks that handle rich 3D point cloud data that lead to more precise and realistic object geometries. However, these models still suffer from inaccuracies due to its nondeterministic/stochastic inferences which could lead to poor performance in grasping scenarios where these errors compound to unsuccessful grasps. We present an approach to calculate the uncertainty of a 3D shape completion model during inference of single view point clouds of an object on a table top. In addition, we propose an update to grasp pose algorithms quality score by introducing the uncertainty of the completed point cloud present in the grasp candidates. To test our full pipeline we perform real world grasping with a 7dof robotic arm with a 2 finger gripper on a large set of household objects and compare against previous approaches that do not measure uncertainty. Our approach ranks the grasp quality better, leading to higher grasp success rate for the rank 5 grasp candidates compared to state of the art.

Figures

Figures reproduced from arXiv: 2504.16183 by the authors.

Figure 1
Figure 1. The proposed approach for measuring uncertainty in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Shape completion and respective uncertainty values [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Adding the uncertainty in the grasp quality score of one grasp candidate (best ranked grasp) for the complete PCD [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: An example of our proposed grasp quality score [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: The rank 5 grasp candidates for each of the three [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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