Pith. sign in

REVIEW 4 major objections 4 minor 46 references

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read Probabilistic PnP lets keypoint grasp networks learn end to end from 3D error, and KGN-Pro shows grasp success rises with it.

desk verdict A credible extension of KGN whose central printed equation drops the confidence weights, so the headline claim needs a fix before it can be trusted. read the letter →

arxiv 2507.14820 v2 pith:D5XCEEJX submitted 2025-07-20 cs.RO

Bingran Chen , Baorun Li , Jian Yang , Yong Liu , Guangyao Zhai This is my paper · ORCID
classification cs.RO
keywords 6-DoFgraspingkeypoint-basedprobabilisticPnPend-to-endlearningconfidencemapRGB-Dgrasppredictionroboticmanipulation3Dsupervision
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

This paper claims that the main bottleneck in keypoint-based 6-DoF robotic grasping is the non-differentiable Perspective-n-Point (PnP) pose solver, which forces earlier keypoint networks to train on 2D keypoints alone. KGN-Pro replaces that solver with a probabilistic PnP layer, turning the weighted sum of squared reprojection errors into a likelihood over poses and matching this pose distribution to ground-truth grasps. A learned 2D confidence map weights each keypoint's contribution, so 3D supervision flows back through the probabilistic layer to the 2D keypoint and confidence predictors, enabling fully end-to-end training. If correct, this makes richer 3D supervision available to any keypoint-based grasp or pose network without giving up the speed and fine-grained control of the 2D representation.

What carries the argument

The key mechanism is the probabilistic PnP layer, adapted from object-pose estimation: the reprojection likelihood is proportional to the exponential of the negative confidence-weighted sum of squared reprojection errors, and the posterior $p(y|X)$ is matched to a target distribution $t(y)$ via a KL divergence loss. The normalizing constant in that loss is estimated by adaptive multiple importance sampling with an optimized proposal $q(y)$, so gradients flow back through the pose distribution to the keypoint and confidence extractors. Nearest-neighbor matching selects the ground-truth grasp that defines $t(y)$, which turns the multi-modal grasp-prediction problem into a well-posed distribution-matching problem.

What would settle it

Compute the variance of the Monte Carlo estimate in Eq. (3) over repeated runs with a fixed network and varying sample count $K$; if increasing $K$ does not stabilize or improve training, or if replacing the learned confidence weights with uniform weights does not change grasp success, the probabilistic-PnP-with-confidence mechanism is not what is driving the reported gains.

Watch

Extended reading notes

Core claim

KGN-Pro's central claim is that modeling the weighted sum of squared reprojection errors probabilistically transmits 3D supervision directly to 2D keypoint predictions and enables end-to-end learning for 6-DoF grasping. The network outputs a keypoint map (center heatmap, sub-pixel offsets, center-to-keypoint offsets) plus a per-keypoint 2D confidence map. The probabilistic PnP layer converts the resulting 2D-3D correspondences into a pose distribution $p(y|X)$, with likelihood $p(X|y) \propto \exp\left(-\tfrac12 \sum_i \|w^{2D}_i E_i(y)\|^2\right)$; a KL-divergence loss against a target distribution centered at the nearest matching ground-truth grasp provides the 3D supervision. The paper reports grasp success rates of about 96% in single-object scenes under loose tolerance and about 80% under the strictest 1 cm, 10° tolerance, consistently above KGN and KGNv2 and clearly above point-cloud methods, with physical experiments exceeding 90% single-object success.

Load-bearing premise

The end-to-end training pipeline rests on the assumption that the finite-sample Monte Carlo estimate of the pose-distribution normalizing constant in Eq. (3) is accurate and low-variance enough that the KL loss gives useful gradients to the keypoint and confidence branches; the paper does not report the number of samples $K$ used.

Editorial extensions

If this is right

  • KGN-Pro's probabilistic PnP converts 3D grasp poses into training signal for 2D keypoint and confidence predictions, removing the non-differentiable bottleneck that limited KGN and KGNv2.
  • The learned 2D confidence map improves pose accuracy by down-weighting unreliable keypoints during reprojection, which the paper credits for the gains under strict 1 cm/10° tolerances.
  • Because the representation stays 2D, the method retains the efficiency and fine-grained grasping of the KGN line while outperforming point-cloud methods like CenterGrasp and Contact-GraspNet, especially on small and cluttered objects.
  • Physical experiments with household objects show overall single-object success above 90% and scene completion rates between 80% and 100% across camera angles, indicating the simulation-trained model transfers to a real robot.

Reading between the lines

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

  • In our reading, the same probabilistic-PnP-plus-confidence pattern could be lifted from grasping to category-level object pose estimation and visual servoing, where keypoint correspondences are also noisy and multi-modal.
  • Because the confidence map is trained only through the KL loss, an ablation that masks or randomizes the confidence weights would reveal how much of the gain comes from weighting versus from the 3D supervision itself; the paper does not isolate these two factors.
  • We suspect the unreported number of importance samples $K$ and the choice of proposal $q(y)$ interact with the claimed gains: at small $K$, the estimator in Eq. (3) may add noise rather than signal, so reporting variance diagnostics would make the mechanism testable.
  • One untested consequence is that the method should degrade gracefully as depth noise increases, since the confidence weights can learn to suppress bad correspondences; a noise-sweep experiment would confirm this.
Share X Bluesky LinkedIn Reddit HN

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 KGN-Pro, a 6-DoF grasp prediction network that preserves the 2D keypoint representation of prior KGN methods and adds a 2D confidence map plus a probabilistic PnP layer. The central claim is that by modeling the weighted sum of squared re-projection errors as a probabilistic object and minimizing a KL divergence between the predicted pose distribution and a nearest-neighbor-matched ground-truth target, the network can be trained end-to-end with 3D supervision, yielding higher grasp success rates than KGN, KGNv2, CenterGrasp, and Contact-GraspNet in simulation and on a physical robot.

Significance. If the central claim holds, the confidence-weighted probabilistic PnP formulation is a potentially generalizable upgrade for keypoint-based 6-DoF grasping, and the paper provides a synthetic benchmark plus a small physical validation. The reported improvements over prior keypoint-based methods at tight precision tolerances are encouraging. However, the paper does not release code or data, reports no variance, and contains a direct inconsistency between Eq. (3) and Eq. (6) that affects whether the confidence map actually participates in the probabilistic PnP loss. The central claim is therefore not yet established as stated.

major comments (4)
  1. [III, Eq. (3); IV-C, Eq. (6)] There is a direct inconsistency between the likelihood used in the Monte Carlo estimate and the likelihood used in the confidence-weighted formulation. Equation (3) defines L_pred as log(1/K Σ_j exp(-1/2 Σ_i ||E_i(y_j)||^2) / q(y_j)), with no confidence weights w_i in the exponent, while Eq. (6) defines p(X|y) ∝ exp(-1/2 Σ_i ||w_i E_i(y)||^2). If the implementation follows Eq. (3), the confidence map does not affect the L_pred term at all, and confidence information reaches the network only through the second term in Eq. (7); the central claim that KGN-Pro 'models the weighted sum of squared re-projection errors probabilistically' (abstract and Section IV-C) is then unsupported. Please correct Eq. (3) to include w_i if that matches the implementation, or explicitly state that the probabilistic term is unweighted and revise the claims accordingly.
  2. [III, Eq. (3)-(4)] The Adaptive Multiple Importance Sampling estimator is underspecified: the paper does not state the number of samples K, the parametric form of the proposal q(y), or the procedure used to optimize q(y). Since Eq. (4) relies on backpropagating importance-weighted samples to approximate the gradient of L_pred, the accuracy of this Monte Carlo estimate and its gradient cannot be verified from the manuscript; please provide these details or an ablation showing the estimator's stability.
  3. [V-B, Table I] Direct3D is plotted in Fig. 4 but is absent from Table I, and no variance or confidence intervals are reported for any of the simulated success rates. The claim of a 'clear advantage' over KGNv2 at tight tolerances cannot be statistically evaluated without repeated runs or confidence intervals; please report means and standard deviations over multiple seeds or trials, and add Direct3D to Table I for consistency with Fig. 4.
  4. [Abstract; V-C] The abstract claims 'grasp cover rate and success rate' as the headline metrics, but 'cover rate' is never defined in the paper. The physical experiment defines GSR and SCR (Scene Completion Rate), and it is unclear whether 'cover rate' refers to SCR or to some other measure; please define the metric precisely and use consistent terminology throughout.
minor comments (4)
  1. [V-A, Eq. (8)] The loss weights are given as λ_H, λ_S, λ_O, λ_KL in Eq. (8), but the implementation text says λ_Y=1, λ_O=1, λ_J=1, λ_KL=0.1; please align the subscript notation and state which symbol multiplies which loss.
  2. [III, Eq. (1)] Equation (1) introduces σ_i, but σ_i disappears in Eq. (6) and is not mentioned again; please clarify whether σ_i is absorbed into the confidence weights w_i or set to a constant.
  3. [V-C, Table II] The table header and rows are visually confusing: the 'Class' column mixes object names (Cylinder, Sphere, etc.) with scene numbers (No.1 to No.5); please restructure the table so that single-object and multi-object results are presented as separate blocks with clear row labels.
  4. [General] Please add a statement on code and data availability, or provide them as supplementary material; the project website is listed, but no repository or dataset link appears in the text.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the core probabilistic-PnP construction is taken from external prior work (EPro-PnP, BPnP) and the headline gains are empirical comparisons against reproduced baselines; the Eq. (3)/Eq. (6) confidence-weight mismatch is a correctness issue, not a circular reduction.

full rationale

The claimed derivation chain is self-contained in the sense required here. The KL loss in Eq. (2) follows from Bayes' rule with a non-informative prior and a Dirac-like target t(y) placed at an externally supplied ground-truth grasp pose; Eq. (6) defines the weighted likelihood, and Eq. (7) is the resulting weighted reprojection objective plus a Monte Carlo log-normalizer. No fitted parameter is renamed as a prediction: the confidence map is trained by the reprojection loss at matched ground-truth poses, and the reported success rates are evaluated against ground-truth offsets on newly generated scenes and physical grasps, not against the training loss. The self-citations by the author team (e.g., SG-Bot, MonoGraspNet, DA2, SecondPose) occur only in related-work context and are not load-bearing; the probabilistic PnP mechanism itself is credited to independent prior work (EPro-PnP [30] and BPnP [35]). A genuine issue exists but is not circularity: Eq. (3) as printed omits the confidence weights w_i that Eq. (6) introduces, and the AMIS proposal q(y), sample count K, and proposal-optimization procedure are unspecified, so whether the confidence map enters L_pred as claimed is unverifiable. This is missing support/inconsistency, not a reduction of a prediction to its inputs, so the circularity score remains 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 1 invented entities

The central training formulation rests on EPro-PnP's probabilistic model, an unverified importance-sampling approximation, and a synthetic six-shape dataset. The paper introduces one new learned component, the confidence map, with no external evidence beyond its own experiments.

free parameters (4)
  • Loss weights lambda_H, lambda_S, lambda_O, lambda_KL = 1, 1, 1, 0.1
    Section V-A sets these empirically; implementation lists lambda_Y, lambda_O, lambda_J, lambda_KL, which do not match Eq. (8). No sensitivity study is reported.
  • Monte Carlo sample count K = not specified
    Eq. (3) approximates the marginal likelihood with K samples; K is never reported.
  • Center-heatmap filtering threshold = not specified
    Section IV-A filters candidate centers with a fixed threshold; the value is not given and affects candidate count and success rate.
  • Re-projection error standard deviation sigma_i in Eq. (1) = not specified
    Defines the likelihood but is never set; the confidence map w2D_i in Eq. (6) plays a similar role.
assumptions (4)
  • domain assumption Probabilistic PnP likelihood and posterior formulation from EPro-PnP [30] carries over to grasp keypoint correspondences.
    Section III defines p(x_i|y) and p(y|X) based on this prior work without re-deriving for the grasping setting.
  • domain assumption The importance-sampling estimator in Eq. (3), with proposal q(y) and K samples, yields gradients accurate enough to train the network.
    Section III relies on this for the KL loss; sample count K and proposal details are unspecified.
  • ad hoc to paper A Dirac-like target distribution t(y) centered at the nearest ground-truth grasp is a valid supervision target for multimodal grasp distributions.
    Introduced in Sections III and IV-C; no analysis of matching errors or of multiple feasible grasps is provided.
  • domain assumption A synthetic dataset of six primitive shapes transfers to unseen real objects with different geometry.
    Section V-A and V-C train on this dataset and claim generalization to household objects.
invented entities (1)
  • 2D confidence map
    purpose: Weights each keypoint during weighted reprojection-error minimization and filters keypoints at inference.
    A learned output introduced by this paper; it has no falsifiable handle outside the reported experiments.

how reviews work

0 comments
Cite this review

Pith. "Pith review of KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning." pith.science (2026). https://pith.science/paper/D5XCEEJX

@misc{pith2026250714820,
  author       = {Pith},
  title        = {Pith review of: KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5XCEEJX}},
  note         = {Machine review of arXiv:2507.14820}
}
read the original abstract

High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor noise, or infer 3D information from RGB images, which introduces expensive annotation requirements and discretization issues. Recent methods mitigate some challenges by retaining a 2D representation to estimate grasp keypoints and applying Perspective-n-Point (PnP) algorithms to compute 6-DoF poses. However, these methods are limited by their non-differentiable nature and reliance solely on 2D supervision, which hinders the full exploitation of rich 3D information. In this work, we present KGN-Pro, a novel grasping network that preserves the efficiency and fine-grained object grasping of previous KGNs while integrating direct 3D optimization through probabilistic PnP layers. KGN-Pro encodes paired RGB-D images to generate Keypoint Map, and further outputs a 2D confidence map to weight keypoint contributions during re-projection error minimization. By modeling the weighted sum of squared re-projection errors probabilistically, the network effectively transmits 3D supervision to its 2D keypoint predictions, enabling end-to-end learning. Experiments on both simulated and real-world platforms demonstrate that KGN-Pro outperforms existing methods in terms of grasp cover rate and success rate.

Figures

Figures reproduced from arXiv: 2507.14820 by the authors.

Figure 1
Figure 1. Schematic comparison between previous KGNs and the proposed [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the proposed KGN-Pro. It takes a pair of RGB-D images as the input and stacks a keypoint extractor to obtain a [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Visualization of grasp pose generated by our method, KGN and CenterGrasp. Our method has a clear advantage regarding generated angle diversity [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: All methods are tested on 50 newly generated scenes to verify the generalization ability of the models on unseen data, covering different objects [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Settings of our physical experiments. The objects within the red box [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

46 extracted references · 40 canonical work pages

  1. [1]

    Sg-bot: Object rearrangement via coarse-to-fine robotic imagination on scene graphs,

    G. Zhai, X. Cai, D. Huang, Y . Di, F. Manhardt, F. Tombari, N. Navab, and B. Busam, “Sg-bot: Object rearrangement via coarse-to-fine robotic imagination on scene graphs,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 4303–4310

    work page 2024

  2. [2]

    Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects,

    W. Liu, C. Paxton, T. Hermans, and D. Fox, “Structformer: Learning spatial structure for language-guided semantic rearrangement of novel objects,” in 2022 International Conference on Robotics and Automa- tion (ICRA) . IEEE, 2022, pp. 6322–6329

    work page 2022

  3. [3]

    Graspgpt: Leveraging semantic knowledge from a large language model for task- oriented grasping,

    C. Tang, D. Huang, W. Ge, W. Liu, and H. Zhang, “Graspgpt: Leveraging semantic knowledge from a large language model for task- oriented grasping,” IEEE Robotics and Automation Letters , vol. 8, no. 11, pp. 7551–7558, 2023

    work page 2023

  4. [4]

    Language embedded radiance fields for zero-shot task-oriented grasping,

    A. Rashid, S. Sharma, C. M. Kim, J. Kerr, L. Y . Chen, A. Kanazawa, and K. Goldberg, “Language embedded radiance fields for zero-shot task-oriented grasping,” in 7th Annual Conference on Robot Learning , 2023

    work page 2023

  5. [5]

    Pointnet: Deep learning on point sets for 3d classification and segmentation,

    C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660

    2017

  6. [6]

    Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

    C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017

    2017

  7. [7]

    Towards scale balanced 6-dof grasp detection in cluttered scenes,

    H. Ma and D. Huang, “Towards scale balanced 6-dof grasp detection in cluttered scenes,” in Conference on robot learning . PMLR, 2023, pp. 2004–2013

    work page 2023

  8. [8]

    Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,

    M. Gou, H.-S. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 459–13 466

    work page 2021

Show all 46 references
  1. [9]

    Monograspnet: 6-dof grasping with a single rgb image,

    G. Zhai, D. Huang, S.-C. Wu, H. Jung, Y . Di, F. Manhardt, F. Tombari, N. Navab, and B. Busam, “Monograspnet: 6-dof grasping with a single rgb image,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 1708–1714

    2023

  2. [10]

    Keypoint-graspnet: Keypoint- based 6-dof grasp generation from the monocular rgb-d input,

    Y . Chen, Y . Lin, R. Xu, and P. A. Vela, “Keypoint-graspnet: Keypoint- based 6-dof grasp generation from the monocular rgb-d input,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7988–7995

    2023

  3. [11]

    Kgnv2: Separating scale and pose prediction for keypoint-based 6-dof grasp synthesis on rgb-d input,

    Y . Chen, R. Xu, Y . Lin, H. Chen, and P. A. Vela, “Kgnv2: Separating scale and pose prediction for keypoint-based 6-dof grasp synthesis on rgb-d input,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 2971–2978

    2023

  4. [12]

    Gknet: Grasp keypoint network for grasp candidates detection,

    R. Xu, F.-J. Chu, and P. A. Vela, “Gknet: Grasp keypoint network for grasp candidates detection,” The International Journal of Robotics Research, vol. 41, no. 4, pp. 361–389, 2022

    2022

  5. [13]

    Glace: Global local accelerated coordinate encoding,

    F. Wang, X. Jiang, S. Galliani, C. V ogel, and M. Pollefeys, “Glace: Global local accelerated coordinate encoding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 562–21 571

    2024

  6. [14]

    Grasp pose detection in point clouds,

    A. Ten Pas, M. Gualtieri, K. Saenko, and R. Platt, “Grasp pose detection in point clouds,” The International Journal of Robotics Research, vol. 36, no. 13-14, pp. 1455–1473, 2017

    2017

  7. [15]

    Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,

    J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kr ¨oger, J. Kuffner, and K. Goldberg, “Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,” in 2016 IEEE intern...

    2016

  8. [16]

    Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach,

    D. Morrison, P. Corke, and J. Leitner, “Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach,” arXiv preprint arXiv:1804.05172, 2018

    2018 arXiv

  9. [17]

    6dof grasp planning by optimizing a deep learning scoring function,

    Y . Zhou and K. Hauser, “6dof grasp planning by optimizing a deep learning scoring function,” in Robotics: Science and systems (RSS) workshop on revisiting contact-turning a problem into a solution , vol. 2, 2017, p. 6

    2017

  10. [18]

    High precision grasp pose detection in dense clutter,

    M. Gualtieri, A. Ten Pas, K. Saenko, and R. Platt, “High precision grasp pose detection in dense clutter,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 598–605

    2016

  11. [19]

    Pointnetgpd: Detecting grasp configurations from point sets,

    H. Liang, X. Ma, S. Li, M. G ¨orner, S. Tang, B. Fang, F. Sun, and J. Zhang, “Pointnetgpd: Detecting grasp configurations from point sets,” in 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019, pp. 3629–3635

    2019

  12. [20]

    Da 2 dataset: Toward dexterity-aware dual- arm grasping,

    G. Zhai, Y . Zheng, Z. Xu, X. Kong, Y . Liu, B. Busam, Y . Ren, N. Navab, and Z. Zhang, “Da 2 dataset: Toward dexterity-aware dual- arm grasping,” IEEE Robotics and Automation Letters , vol. 7, no. 4, 2022

    2022

  13. [21]

    Grasping in the wild: Learning 6dof closed-loop grasp- ing from low-cost demonstrations,

    T. Zahavy, D. Novotny, M. Goldblum, L. Davis, A. Wang, B. Maciel- Pearson et al., “Grasping in the wild: Learning 6dof closed-loop grasp- ing from low-cost demonstrations,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 1446–1453

    2020

  14. [22]

    Learning synergies between pushing and grasping with self- supervised deep reinforcement learning,

    A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. Funkhouser, “Learning synergies between pushing and grasping with self- supervised deep reinforcement learning,” in 2018 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 4238–4245

    2018

  15. [23]

    Reinforcement learning based pushing and grasping objects from ungraspable poses,

    Y . Yang, Y . Zhu, J. Luo, and B. Yuan, “Reinforcement learning based pushing and grasping objects from ungraspable poses,” IEEE Transactions on Robotics , vol. 36, no. 5, pp. 1367–1382, 2020

    2020

  16. [24]

    Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,

    L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” 2016 IEEE international conference on robotics and automation (ICRA) , pp. 3406–3413, 2016

    2016

  17. [25]

    Learning dexterous manipulation from exemplar object trajectories and pre-grasps,

    P. Mandikal, A. Ablavatski, and A. Kowdle, “Learning dexterous manipulation from exemplar object trajectories and pre-grasps,” in 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 14 081–14 087

    2021

  18. [26]

    Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,

    M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact- graspnet: Efficient 6-dof grasp generation in cluttered scenes,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 438–13 444

    2021

  19. [27]

    kpam: Keypoint affordances for category-level robotic manipulation,

    L. Manuelli, W. Gao, P. Florence, and R. Tedrake, “kpam: Keypoint affordances for category-level robotic manipulation,” in The Interna- tional Symposium of Robotics Research . Springer, 2019, pp. 132– 157

    2019

  20. [28]

    A data-driven statistical frame- work for post-grasp manipulation,

    Y . Li, M. Paolini, and A. Rodriguez, “A data-driven statistical frame- work for post-grasp manipulation,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2015, pp. 5434– 5441

    2015

  21. [29]

    Control-based grasp planning with contact points,

    V . Kumar and A. Garg, “Control-based grasp planning with contact points,” IEEE Transactions on Robotics , vol. 30, no. 2, pp. 356–367, 2014

    2014

  22. [30]

    Epro- pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation,

    H. Chen, P. Wang, F. Wang, W. Tian, L. Xiong, and H. Li, “Epro- pnp: Generalized end-to-end probabilistic perspective-n-points for monocular object pose estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 2781–2790

    2022

  23. [31]

    Adaptive multiple importance sampling,

    J.-M. CORNUET, J.-M. MARIN, A. Mira, and C. P. Robert, “Adaptive multiple importance sampling,” Scandinavian Journal of Statistics , vol. 39, no. 4, pp. 798–812, 2012

    2012

  24. [32]

    Epnp: An accurate o(n) solution to the pnp problem,

    V . Lepetit, F. Moreno-Noguer, and P. Fua, “Epnp: An accurate o(n) solution to the pnp problem,” International journal of computer vision , vol. 81, pp. 155–166, 2009

    2009

  25. [33]

    Normalized object coordinate space for category-level 6d object pose and size estimation,

    H. Wang, S. Sridhar, J. Huang, J. Valentin, S. Song, and L. J. Guibas, “Normalized object coordinate space for category-level 6d object pose and size estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2642–2651

    2019

  26. [34]

    Secondpose: Se (3)-consistent dual-stream feature fusion for category-level pose estimation,

    Y . Chen, Y . Di, G. Zhai, F. Manhardt, C. Zhang, R. Zhang, F. Tombari, N. Navab, and B. Busam, “Secondpose: Se (3)-consistent dual-stream feature fusion for category-level pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogni- tion...

    2024

  27. [35]

    End-to-end learnable geometric vision by backpropagating pnp optimization,

    B. Chen, A. Parra, J. Cao, N. Li, and T.-J. Chin, “End-to-end learnable geometric vision by backpropagating pnp optimization,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 8100–8109

    2020

  28. [36]

    Deep layer aggrega- tion,

    F. Yu, D. Wang, E. Shelhamer, and T. Darrell, “Deep layer aggrega- tion,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 2403–2412

    2018

  29. [37]

    Deformable convolutional networks,

    J. Dai, H. Qi, Y . Xiong, Y . Li, G. Zhang, H. Hu, and Y . Wei, “Deformable convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 764–773

    2017

  30. [38]

    6-dof graspnet: Variational grasp generation for object manipulation,

    A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 2901–2910

    2019

  31. [39]

    Learning 6-dof grasping interaction via deep geometry- aware 3d representations,

    X. Yan, J. Hsu, M. Khansari, Y . Bai, A. Pathak, A. Gupta, J. Davidson, and H. Lee, “Learning 6-dof grasping interaction via deep geometry- aware 3d representations,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 3766–3773

    2018

  32. [40]

    Jacquard: A large scale dataset for robotic grasp detection,

    A. Depierre, E. Dellandr ´ea, and L. Chen, “Jacquard: A large scale dataset for robotic grasp detection,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 3511–3516

    2018

  33. [41]

    Primitive shape recognition for object grasping,

    Y . Lin, C. Tang, F.-J. Chu, R. Xu, and P. A. Vela, “Primitive shape recognition for object grasping,” arXiv preprint arXiv:2201.00956 , 2022

    2022 arXiv

  34. [42]

    Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes,

    Y . Xiang, T. Schmidt, V . Narayanan, and D. Fox, “Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes,” arXiv preprint arXiv:1711.00199 , 2017

    2017 arXiv

  35. [43]

    Catgrasp: Learning category-level task-relevant grasping in clutter from simulation,

    B. Wen, W. Lian, K. Bekris, and S. Schaal, “Catgrasp: Learning category-level task-relevant grasping in clutter from simulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6401–6408

    2022

  36. [44]

    6d robotic grasping system using convolutional neural networks and adaptive artificial potential fields with orientation control,

    C. C. B. Viturino, D. M. de Oliveira, A. G. S. Conceic ¸ ˜ao, and U. Ju- nior, “6d robotic grasping system using convolutional neural networks and adaptive artificial potential fields with orientation control,” in 2021 Latin American Robotics Symposium (LARS), 2021 Brazilian S...

    2021

  37. [45]

    Centergrasp: Object-aware implicit representation learning for simultaneous shape reconstruction and 6-dof grasp estimation,

    E. Chisari, N. Heppert, T. Welschehold, W. Burgard, and A. Val- ada, “Centergrasp: Object-aware implicit representation learning for simultaneous shape reconstruction and 6-dof grasp estimation,” IEEE Robotics and Automation Letters , 2024

    2024

  38. [46]

    Acronym: A large-scale grasp dataset based on simulation,

    C. Eppner, A. Mousavian, and D. Fox, “Acronym: A large-scale grasp dataset based on simulation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 6222–6227

    2021

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.