Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation

Jacky Liang; Jialiang Zhao; Oliver Kroemer

REVIEW 4 major objections 5 minor 24 references

Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A robot can grasp precisely by predicting how the object will shift during the grasp, then compensating.

desk verdict A sound, incremental contribution on probabilistic post-grasp displacement prediction that deserves a serious referee, but the headline real-world numbers only measure planar motion, so the 'precise grasping' claim is narrower than stated. read the letter →

arxiv 1909.02129 v1 pith:WKLK5CLJ submitted 2019-09-04 cs.RO cs.CVcs.LG

Jialiang Zhao , Jacky Liang , Oliver Kroemer This is my paper · ORCID

classification cs.ROcs.CVcs.LG

keywords precisegraspingpost-graspdisplacementgraspqualityvariancepredictionsim-to-realdepthimagesindustrialpartsGaussianmodel

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 proposes that precise robotic grasping does not require eliminating object motion during a grasp; it requires predicting that motion. The authors train two convolutional networks in simulation on over a thousand industrial CAD parts: one predicts the chance a grasp lifts the object, the other predicts the mean and variance of the object's post-grasp displacement from a depth image and grasp parameters. On a real robot with novel 3D-printed parts, the best displacement model reaches a mean translational error of 0.68 cm and rotational error of 3.42 degrees, and selecting low-variance grasps makes those errors more consistent. The practical stake is that assembly and palletizing tasks can use the predicted mean displacement as a correction instead of relying on a rigid or precisely repeatable grasp.

What carries the argument

The load-bearing object is the Grasp Displacement Network (GDN), a convolutional network that maps a depth image cropped to the grasp and aligned with its rotation, plus the grasp's offset from the object center, to a four-dimensional Gaussian over post-grasp displacement in the object frame. It is trained with a negative log-likelihood loss that includes the variance outputs, which lets the network raise its uncertainty on inherently variable grasps instead of fitting a single mean. A companion Grasp Quality Network, trained first and used to initialize the GDN's convolutional filters, restricts planning to the top 3% of grasps by predicted lift success; the planner then chooses the lowest-variance grasp among those. This two-stage selection is what turns a displacement predictor into a precise-grasp planner.

What would settle it

Repeat the experiment with a motion-capture or depth-based 6-DOF tracker while the object is grasped. If the predicted planar displacement corrections miss measured out-of-plane motion or the full-pose error exceeds the reported 0.68 cm and 3.42 degrees, the central claim is limited to planar cases.

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Extended reading notes

Core claim

The paper's central claim is that post-grasp displacement is a learnable random variable: for a top-down parallel-jaw grasp on a flat surface, the displacement of the object can be modeled as a Gaussian whose mean and diagonal variance are functions of the grasp and a 64x64 depth image. The Grasp Displacement Network learns these functions by maximizing the log-likelihood of simulated successful grasps, and the planner filters for the top 3% of grasps by predicted lift success, then picks the one with the smallest predicted displacement variance. The prediction is converted from grasp-frame to object-frame displacement and used as a compensation when placing the object. The reported real-world results, 0.68 cm and 3.42 degrees RMSE on novel objects, are the evidence that this distribution-based strategy supports precise placement.

Load-bearing premise

The real-world precision numbers assume the object moves only in the table plane, and the tape-marker measurement leaves out vertical motion and tilts, so the method is validated for planar placement rather than general 6-DOF displacement.

Editorial extensions

If this is right

Precise placement no longer requires a grasp that holds the object rigidly; a predictable shift can be corrected at placement time.
In real-world trials, planning for high quality and low variance reduced translation RMSE from 0.72 to 0.68 cm and rotation RMSE from 3.79 to 3.42 degrees while shrinking the spread across objects.
Simulation-only training on depth images transfers to a real robot without fine-tuning, so the pipeline could be applied to new grippers or depth sensors by regenerating simulated grasps.
The predicted mean displacement can offset the target pose in applications such as palletizing, where the robot must place objects at a known position and orientation.

Reading between the lines

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

The tape-marker evaluation assumes planar motion, so the paper's precision claim is directly established only for objects that slide without lifting or tilting; a 6-DOF motion-capture test would show how far the method generalizes.
Predicted variance can be read as a learned measure of grasp repeatability, which might also guide viewpoint selection or trigger a re-grasp when no low-variance grasp exists.
The same probabilistic displacement loss could be applied to other manipulation outcomes, such as pushing or insertion corrections, whenever a planner needs to select actions with predictable consequences.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Core take: the paper does something new and useful. It trains a CNN to predict a Gaussian distribution over post-grasp displacement and uses the predicted variance to select low-variance, high-quality grasps. The entire pipeline is trained in simulation and deployed on a real Franka Panda without fine-tuning, which is a genuine practical achievement. The real-world numbers (0.68 cm translation, 3.42 deg rotation) are the headline, but they only describe in-plane translation and yaw rotation. The paper's own problem statement includes Δz, so the measured quantity in the real world is not the modeled quantity. The authors are transparent about the planar assumption, but the conclusion 'precise robotic grasping' is broader than the evidence supports. That is the paper's real soft spot, and it is not minor for palletizing or assembly where vertical placement and out-of-plane tilt matter. What the paper does well: the MLE loss is correct, the architecture choices are sensible, and the sim dataset is large (over 1M grasp attempts across 1011 CAD models). The observation that including variance prediction improves the mean prediction is a nice empirical result, and the comparison among full-image vs grasp-centric patches and mean-only vs mean+variance is clearly organized. Sim-to-real transfer without fine-tuning works, which is the strongest practical claim. Other issues, in proportion: the simulation evaluation of the GDN includes 50 objects from the training set among 85 total, though 35 held-out objects are also reported. The object filtering by grasp difficulty is post hoc and narrows the evaluation set. The real-world evaluation uses only seven objects, so generalization is thin. There is also no comparison against prior precision-grasping baselines like Chen et al., so we don't know the competitive position. These are real gaps but not a broken core. The method is clearly described, the math checks out, and the planar limitation is a scope restriction rather than a logical flaw. This paper is for people working on task-oriented grasping, grasp planning under uncertainty, and depth-only sim-to-real transfer. It is not a paradigm shift, but it is a sound incremental contribution and the variance-based grasp selection is a useful idea. A serious referee should engage with it; a good review would push for a full 6-DOF real-world evaluation and at least one baseline comparison. That would turn a plausible planar result into a convincingly precise one.

Referee Report

4 major / 5 minor

Summary. The paper addresses the problem of planning robotic grasps that yield a known and controllable post-grasp object pose. It trains two convolutional networks in simulation from 1011 CAD models: a Grasp Quality Network (GQN) predicting lift success, and a Grasp Displacement Network (GDN) predicting a Gaussian distribution over post-grasp displacement (mean and variance) from a depth image and grasp parameters. A planner filters grasps by predicted quality (top 3%) and then selects the grasp with the lowest predicted displacement variance; the predicted mean displacement is used to compensate downstream placement. The method is evaluated in simulation against LOWESS and ablated GDN variants, and on a real Franka Panda with seven novel 3D-printed objects. Headline results are a real-world translation RMSE of 0.68 cm and rotation RMSE of 3.42 deg for the GCIP-M+V GDN under high-quality, low-variance grasp selection.

Significance. If the results hold, the paper makes a useful contribution: it reformulates precision grasping as probabilistic displacement prediction, uses a correct heteroscedastic regression loss, and gives evidence that selecting low-variance grasps improves prediction consistency. The depth-only, simulation-to-real deployment without fine-tuning is a strength, and the paper states that datasets and supplementary material are available, which supports reproducibility. However, the evaluation as presented establishes only planar (2-D plus yaw) pose accuracy in the real world, and the simulation numbers mix training and validation objects; the central 'precise grasping' claim is therefore narrower than stated. With additional experiments quantifying full 6-DOF post-grasp error and a comparison to an existing precision-grasping baseline, the contribution would be solid.

major comments (4)

[§3.1, §4.4] The real-world evaluation measures only planar displacement. The problem statement defines Δp = [Δx, Δy, Δz, Δθ]^T and Eqs. (1)-(2) model a distribution over all four components, but §4.4 estimates displacement from two tape markers on top of the object, which can recover at most (Δx, Δy, Δθ) under the stated assumption that displacements occur only in a plane. Therefore the reported 0.68 cm translation RMSE and 3.42° rotation RMSE are in-plane errors, not 3-D pose errors; Δz and out-of-plane tilt are unmeasured. Since the motivating applications are palletizing and assembly, for which vertical placement and out-of-plane orientation matter, the headline claim of 'precise grasping' is not yet supported. Please report full 6-DOF post-grasp pose error (e.g., with an external tracker or CAD-model registration with symmetry handling), or at least provide an estimate or bound on Δz, and re-state the claims to the planar setting if this is not possible.
[§4.3] The simulation evaluation of the GDN uses 85 objects, of which 50 come from the training dataset and 35 from the validation set. Aggregating RMSE over this mixture can mask the method's performance on genuinely novel objects, especially since the planner's grasp selection is also learned. The paper should report separate results for training, validation, and held-out objects, and the headline simulation numbers should be based only on objects not seen during training or model selection. The real-world experiment already uses seven novel objects, but it is subject to the planar-measurement limitation in Major Comment 1.
[§4.4.1, Fig. 8] The palletizing application is demonstrated qualitatively with a single image and no quantitative placement error. To support the claim that the predicted displacement can be used to compensate for downstream tasks, the paper should measure the final placed-pose error (translation and rotation) for compensated versus uncompensated placements over multiple trials. Without this, the application-level benefit of the low-variance grasp selection is not established.
[§4.1] The baseline comparison is insufficient to support the claim of a precise-grasping contribution. LOWESS is limited to known objects and cannot generalize, and the remaining comparisons are ablations of the proposed method; no existing precision-grasping method (e.g., the probabilistic analytical approach of Chen et al. [4], or a Dex-Net-based planner [17]) is compared. A comparison to at least one prior precision-oriented grasp planner is needed to substantiate the 'precise robotic grasping' claim in the title and abstract.

minor comments (5)

[Abstract] The abstract says 'mean prediction errors of 0.68cm and 3.42deg' but the body reports root mean square error; please use consistent terminology.
[Eqs. (4)-(5)] The factor of 1/2 in Eq. (4) is dropped in Eq. (5); this is a constant scaling and does not affect the optimum, but the two equations should be reconciled so that the loss is written consistently.
[§3.3] There is a duplicated word in the sentence 'The robot robot executes a grasp g'.
[§4.4.1] The sentence introducing the palletizing example refers to 'Figure 7', but the palletizing application is shown in Figure 8.
[Figs. 4-7] Please state the units of the reported RMSE values in the axis labels or captions, and consider reporting per-object error distributions or a paired significance test, since the number of real-world trials is modest.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the displacement estimator is trained on simulated post-grasp displacements and evaluated against held-out simulation and real-world measurements, with no fitted parameter or self-citation chain forcing the reported results.

full rationale

The paper's derivation chain is self-contained. The Grasp Displacement Network is trained by maximizing the Gaussian log-likelihood of simulated post-grasp displacements (Eqs. 2-5 in Section 3.4), and the real-world evaluation in Section 4.4 uses 7 novel 3D-printed objects that were not used in training, with no fine-tuning on real data. No predicted quantity is reused as a training target for the same model, and no fitted parameter is renamed as a prediction. The LOWESS baseline uses hand-selected kernel bandwidth parameters, but it serves only as a comparison method; its parameters do not enter the GDN training or the reported GDN errors. The self-citations in the paper, e.g., Reference [17] which includes co-author J. Liang and Reference [3] which includes co-author O. Kroemer, are used as background for depth-based grasping and tactile localization, not as the authority for the displacement distribution or for the planner's optimality. The tape-marker limitation acknowledged in Section 4.4, where real-world displacement is measured assuming planar motion and therefore does not validate the predicted delta-z component, is a real-world validity gap rather than a circularity: the model predicts a 4-DOF displacement while the real experiment measures only the planar components. That distinction narrows the scope of the claim but does not make the derivation depend on its own conclusion. Overall, the central claim is supported by external held-out measurements and does not reduce by construction to its inputs.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical entities. The free parameters are standard training and planning hyperparameters; the axioms are domain restrictions that define the problem scope. The most consequential assumption is the planar-displacement measurement in real-world evaluation.

free parameters (4)

LOWESS kernel bandwidth = diag([0.02, 0.02, 0.05, 1.00])
Hand-chosen variance for the Gaussian kernel in the LOWESS baseline (Section 4.1). Not used by the GDN, but affects the reported baseline.
Depth image noise std = 3 mm
Added to simulated depth images to mimic real sensors (Section 4.2). A modeling choice that affects sim2real transfer.
Grasp quality top percentile = 3%
Grasp planner selects top 3% of scored grasps before minimizing variance (Section 3.4). Affects the trade-off between robustness and precision.
Object filtering thresholds = success rate 5-40%, size 2-15 cm
Objects outside these ranges were removed before training (Section 3.3). This biases the training distribution towards neither-too-hard nor too-easy, mid-size objects.

assumptions (4)

domain assumption Post-grasp displacement follows a Gaussian distribution.
Equation (1) assumes delta g ~ N(mu, sigma^2), and the loss is derived from this. The true distribution may be multimodal or non-Gaussian.
domain assumption Object displacements during real robot experiments occur only in a plane.
Marker-based measurement in Section 4.4 assumes planar translation/rotation; delta z is ignored in evaluation.
domain assumption Simulation objects have uniform density and identical coefficient of friction.
Section 3.3 states this assumption; real objects may vary, affecting sim2real transfer.
domain assumption Top-down parallel-jaw grasps on singulated objects on a flat surface.
Problem statement in Section 3.1 restricts scope; results do not generalize to other grasp types or clutter.

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Cite this review

Pith. "Pith review of Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation." pith.science (2026). https://pith.science/paper/WKLK5CLJ

@misc{pith2026190902129,
  author       = {Pith},
  title        = {Pith review of: Towards Precise Robotic Grasping by Probabilistic Post-grasp Displacement Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WKLK5CLJ}},
  note         = {Machine review of arXiv:1909.02129}
}

read the original abstract

Precise robotic grasping is important for many industrial applications, such as assembly and palletizing, where the location of the object needs to be controlled and known. However, achieving precise grasps is challenging due to noise in sensing and control, as well as unknown object properties. We propose a method to plan robotic grasps that are both robust and precise by training two convolutional neural networks - one to predict the robustness of a grasp and another to predict a distribution of post-grasp object displacements. Our networks are trained with depth images in simulation on a dataset of over 1000 industrial parts and were successfully deployed on a real robot without having to be further fine-tuned. The proposed displacement estimator achieves a mean prediction errors of 0.68cm and 3.42deg on novel objects in real world experiments.

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Reference graph

Works this paper leans on

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [4]

Journal of Intelligent & Robotic Systems 90(1-2):19–43

Chen D, Dietrich V , Liu Z, V on Wichert G (2018) A probabilistic framework for uncertainty-aware high-accuracy precision grasping of unknown objects. Journal of Intelligent & Robotic Systems 90(1-2):19–43

work page 2018

[2] [17]

Robotics: Science and Systems (RSS)

Mahler J, Liang J, Niyaz S, Laskey M, Doan R, Liu X, Ojea JA, Goldberg K (2017) Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics. Robotics: Science and Systems (RSS)

work page 2017

[3] [1]

IEEE Transactions on Robotics 30(2):289–309

Bohg J, Morales A, Asfour T, Kragic D (2014) Data-driven grasp synthesisa survey. IEEE Transactions on Robotics 30(2):289–309

work page 2014

[4] [2]

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Bousmalis K, Silberman N, Dohan D, Erhan D, Krishnan D (2017) Unsu- pervised pixel-level domain adaptation with generative adversarial networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

work page 2017

[5] [3]

In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp 3368–3375

Chebotar Y , Kroemer O, Peters J (2014) Learning robot tactile sensing for ob- ject manipulation. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, pp 3368–3375

work page 2014

[6] [5]

In: International Symposium on Experimental Robotics, Springer, pp 601–612

Choi C, Del Preto J, Rus D (2016) Using vision for pre-and post-grasping ob- ject localization for soft hands. In: International Symposium on Experimental Robotics, Springer, pp 601–612

work page 2016

[7] [6]

Journal of the American statistical association 74(368):829–836

Cleveland WS (1979) Robust locally weighted regression and smoothing scat- terplots. Journal of the American statistical association 74(368):829–836

work page 1979

[8] [7]

In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 3266–3273

Detry R, Papon J, Matthies L (2017) Task-oriented grasping with semantic and geometric scene understanding. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, pp 3266–3273

work page 2017