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REVIEW 2 major objections 2 minor 1 cited by

GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping

T0 review · 2 major / 2 minor · reviewed 2026-06-26 · grok-4.3

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Pith's one-line read Affordance fields guide camera adjustment to improve task-oriented grasp success without scene reconstruction.

desk verdict The paper's main move is feeding the affordance field straight into the view planner as the information metric, skipping reconstruction, but this only works if the initial occluded prediction is already useful. read the letter →

arxiv 2606.19091 v1 pith:II7SMSGJ submitted 2026-06-17 cs.RO

Zanjia Tong , Wenlong Dong , Chengjie Zhang , Hong Zhang This is my paper · ORCID
classification cs.RO
keywords task-orientedgraspingaffordancepredictionviewplanningactiveperceptionroboticocclusionhandlinggraspevaluation
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

Task-oriented grasping performance drops when occlusions hide relevant object parts in the initial view. The paper builds GCNGrasp-VP around GCNGrasp-v2, a model that predicts both grasp quality and an affordance field in constant time. It then feeds the affordance field into Affordance-VP as a direct information-gain signal that steers the camera toward task-relevant regions. This produces better view planning than uncertainty baselines after one adjustment and raises real-world grasp rates while running at millisecond latency.

What carries the argument

Affordance-VP, which treats the affordance field from GCNGrasp-v2 as an information-gain metric to select camera poses that observe task-relevant object regions.

What would settle it

A side-by-side test on occluded objects that measures grasp success after exactly one view change: if the affordance-guided planner does not produce higher success rates than an uncertainty-driven planner, the central claim is falsified.

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Extended reading notes

Core claim

GCNGrasp-VP integrates affordance field prediction with active view planning so that the field output by GCNGrasp-v2 serves as an information-gain metric; Affordance-VP therefore directs the camera to task-relevant areas without scene reconstruction, yielding superior planning performance and higher grasp success in occluded single-object settings.

Load-bearing premise

The affordance field from GCNGrasp-v2 can be used directly as a reliable information-gain metric that directs the camera to task-relevant regions without scene reconstruction or extra uncertainty modeling.

Editorial extensions

If this is right

  • View planning outperforms scene-uncertainty-driven baselines after only one view adjustment.
  • Grasp success rates increase in single-object real-world scenarios.
  • Computational latency stays at millisecond level during planning and inference.

Reading between the lines

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

  • Semantic affordance signals could substitute for geometric uncertainty in other active-perception robotic tasks.
  • Constant-time affordance prediction may support repeated view adjustments in time-sensitive manipulation pipelines.
  • The single-object gains suggest the planner could be tested on partial occlusions in multi-object scenes to check scaling.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper introduces GCNGrasp-VP, which combines GCNGrasp-v2 (a model for simultaneous grasp evaluation and affordance field prediction with constant-time inference) and an Affordance-guided View Planner (Affordance-VP). The planner treats the affordance field directly as an information-gain metric to select a single next view that reveals task-relevant regions, avoiding scene reconstruction or explicit uncertainty modeling. Experiments claim statistically significant outperformance over scene-uncertainty baselines on view planning and higher real-world grasp success rates in single-object scenarios, all at millisecond latency.

Significance. If the central construction holds, the work offers a lightweight alternative to reconstruction-heavy active perception pipelines by reusing task-semantic predictions for view selection. The public release of code and models strengthens reproducibility. The single-adjustment regime and constant-time inference are practically relevant for real-time robotic manipulation.

major comments (2)
  1. [Abstract and §3 (method description)] The headline performance claims (one-view outperformance and grasp-rate gains) rest on the assumption that GCNGrasp-v2’s affordance field remains a faithful proxy for task-relevant information even when the initial view occludes those regions. The manuscript provides no ablation or analysis showing that the field does not degrade precisely in the occluded regime the planner is introduced to correct; without such evidence the superiority over uncertainty baselines does not necessarily follow from the stated construction.
  2. [Abstract and experimental section] Real-world validation is reported only for single-object scenarios. The paper does not demonstrate whether the affordance-as-gain signal scales when multiple objects create mutual occlusions or when task semantics are distributed across several items, which limits the generality of the “substantial improvements” claim.
minor comments (2)
  1. [Abstract] The abstract states “significantly outperforms” and “substantial improvements” without quoting the exact metrics, number of trials, or statistical tests; these numbers should appear in the abstract or be clearly referenced to the results tables.
  2. [§3] Notation for the affordance field and the information-gain formulation should be introduced with explicit equations rather than prose descriptions to allow readers to verify the claimed constant-time property.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment point by point below.

read point-by-point responses

  1. Referee: [Abstract and §3 (method description)] The headline performance claims (one-view outperformance and grasp-rate gains) rest on the assumption that GCNGrasp-v2’s affordance field remains a faithful proxy for task-relevant information even when the initial view occludes those regions. The manuscript provides no ablation or analysis showing that the field does not degrade precisely in the occluded regime the planner is introduced to correct; without such evidence the superiority over uncertainty baselines does not necessarily follow from the stated construction.

    Authors: We acknowledge the absence of a dedicated ablation on affordance-field degradation under occlusion. The reported view-planning and grasp-success results demonstrate that Affordance-VP outperforms uncertainty baselines, providing indirect evidence that the field remains useful in the evaluated occluded cases. To directly address the concern we will add a short analysis subsection comparing affordance predictions to available occluded ground-truth task regions and discuss the assumption explicitly. revision: yes

  2. Referee: [Abstract and experimental section] Real-world validation is reported only for single-object scenarios. The paper does not demonstrate whether the affordance-as-gain signal scales when multiple objects create mutual occlusions or when task semantics are distributed across several items, which limits the generality of the “substantial improvements” claim.

    Authors: The abstract and experimental section explicitly limit real-world validation to single-object scenarios, and the “substantial improvements” claim is scoped to that setting. Multi-object mutual-occlusion cases are a natural extension but lie outside the current scope, which targets efficient single-adjustment task-oriented grasping. No change to the claims or scope is required. revision: no

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation remains self-contained

full rationale

The abstract and described framework present GCNGrasp-VP as an integration of an existing affordance prediction model (GCNGrasp-v2) with a view planner that directly adopts the affordance field as an information-gain signal. No equations, fitted parameters, or self-referential definitions are exhibited that would reduce any claimed prediction or uniqueness result to the inputs by construction. The central performance claims rest on empirical outperformance and real-world validation rather than on any load-bearing self-citation chain or ansatz smuggled via prior work. The derivation chain is therefore independent of the target results.

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

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that the learned affordance field is a sufficient proxy for task-relevant information gain.

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0 comments
Cite this review

Pith. "Pith review of GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping." pith.science (2026). https://pith.science/paper/II7SMSGJ

@misc{pith2026260619091,
  author       = {Pith},
  title        = {Pith review of: GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/II7SMSGJ}},
  note         = {Machine review of arXiv:2606.19091}
}
read the original abstract

Task-oriented grasping performance degrades significantly when object views suffer from occlusions. Existing task-oriented grasping methods typically assume task-relevant regions are visible in the initial frame, while view planning approaches enable active perception but often ignore task semantics and rely on time-consuming scene reconstruction. To address these limitations, we present GCNGrasp-VP, an efficient framework integrating affordance field prediction with active view planning. Central to this framework is GCNGrasp-v2, a task-oriented grasp model that simultaneously supports grasp evaluation and affordance field prediction, achieving constant-time inference complexity. Leveraging this capability, our Affordance-guided View Planner (Affordance-VP) utilizes the affordance field as an information gain metric to guide camera observation of task-relevant regions without requiring scene reconstruction. View planning results show that our method significantly outperforms scene-uncertainty-driven baselines with only one view adjustment. Real-world validation further confirms substantial improvements in grasp success rates for single-object scenarios while maintaining millisecond-level computational latency. Code and models are available at https://github.com/Instinct323/GCNGrasp-VP.

Figures

Figures reproduced from arXiv: 2606.19091 by the authors.

Figure 1
Figure 1. Qualitative comparison of task-oriented grasping results after acquiring additional views using different view planners. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Overview of the GCNGrasp-VP architecture. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Overview of the GCNGrasp architecture and input definitions, where [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Overview of the experimental system pipeline and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FUSE: Active Functional Affordance Grounding through Adaptive Semantic-Geometric Evidence Acquisition

    cs.RO 2026-08 conditional novelty 7.0 of 10

    FUSE, an entropy-gated planner that combines amortized viewpoint prediction with explicit semantic-geometric exploration, achieves the best non-oracle active functional grounding results on a new Habitat benchmark whi...

Reference graph

Works this paper leans on

39 extracted references · cited by 1 Pith paper

  1. [1]

    Same object, different grasps: Data and semantic knowledge for task-oriented grasping,

    A. Murali, W. Liu, K. Marino,et al., “Same object, different grasps: Data and semantic knowledge for task-oriented grasping,” inProceed- ings of the 2020 Conference on Robot Learning. PMLR, Oct. 2021, pp. 1540–1557

    2020

  2. [2]

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

    C. Tang, D. Huang, W. Ge,et al., “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, Nov. 2023

    2023

  3. [3]

    Learning transferable visual models from natural language supervision,

    A. Radford, J. W. Kim, C. Hallacy,et al., “Learning transferable visual models from natural language supervision,” inInternational Conference on Machine Learning. PMLR, 2021, pp. 8748–8763

    2021

  4. [4]

    GPT-4 technical report,

    OpenAI, J. Achiam, S. Adler,et al., “GPT-4 technical report,” 2023

    2023

  5. [5]

    Lan-grasp: Using large language models for semantic object grasping and placement,

    R. Mirjalili, M. Krawez, Y . Blei,et al., “Lan-grasp: Using large language models for semantic object grasping and placement,” 2023

    2023

  6. [6]

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

    A. Rashid, S. Sharma, C. M. Kim,et al., “Language embedded radiance fields for zero-shot task-oriented grasping,” in7th Annual Conference on Robot Learning, Aug. 2023

    2023

  7. [7]

    ShapeGrasp: Zero-shot task- oriented grasping with large language models through geometric decomposition,

    S. Li, S. Bhagat, J. Campbell,et al., “ShapeGrasp: Zero-shot task- oriented grasping with large language models through geometric decomposition,” in2024 IEEE/RSJ International Conference on Intel- ligent Robots and Systems (IROS). Abu Dhabi, United Arab Emirates: IEEE, Oct. 2024, pp. 10 527–10 534

    2024

  8. [8]

    Open-vocabulary part-based grasping,

    T. Van Oor, “Open-vocabulary part-based grasping,” Ph.D. disserta- tion, Queensland University of Technology, 2024

    2024

Show all 39 references
  1. [9]

    Leveraging semantic and geometric information for zero-shot robot-to-human handover,

    J. Liu, W. Dong, J. Wang,et al., “Leveraging semantic and geometric information for zero-shot robot-to-human handover,” in2025 IEEE In- ternational Conference on Robotics and Automation (ICRA). Atlanta, GA, USA: IEEE, May 2025, pp. 16 340–16 346

    2025

  2. [10]

    Robo-ABC: Affordance generalization beyond categories via semantic correspondence for robot manipula- tion,

    Y . Ju, K. Hu, G. Zhang,et al., “Robo-ABC: Affordance generalization beyond categories via semantic correspondence for robot manipula- tion,” inComputer Vision – ECCV 2024, A. Leonardis, E. Ricci, S. Roth,et al., Eds. Cham: Springer Nature Switzerland, 2025, vol. 15099, pp. 222–239

    2024

  3. [11]

    GRIM: Task-oriented grasping with conditioning on generative examples,

    Shailesh, A. Raj, N. Kumar,et al., “GRIM: Task-oriented grasping with conditioning on generative examples,”Proceedings of the AAAI Conference on Artificial Intelligence, vol. 40, no. 22, pp. 18 118– 18 125, Mar. 2026

    2026

  4. [12]

    RTAGrasp: Learning task-oriented grasping from human videos via retrieval, transfer, and alignment,

    W. Dong, D. Huang, J. Liu,et al., “RTAGrasp: Learning task-oriented grasping from human videos via retrieval, transfer, and alignment,” in2025 IEEE International Conference on Robotics and Automation (ICRA). Atlanta, GA, USA: IEEE, May 2025, pp. 1–7

    2025

  5. [13]

    Closed-loop next-best-view planning for target-driven grasping,

    M. Breyer, L. Ott, R. Siegwart,et al., “Closed-loop next-best-view planning for target-driven grasping,” in2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Kyoto, Japan: IEEE, Oct. 2022, pp. 1411–1416

    2022

  6. [14]

    Active-perceptive language-oriented grasp policy for heavily cluttered scenes,

    Y . Dai, S. Chen, K. Yang,et al., “Active-perceptive language-oriented grasp policy for heavily cluttered scenes,”IEEE Robotics and Automa- tion Letters, vol. 10, no. 11, pp. 11 094–11 101, Nov. 2025

    2025

  7. [15]

    ActiveVLA: Injecting active perception into vision-language-action models for precise 3D robotic manipula- tion,

    Z. Liu, Y . Gu, Y . Wang,et al., “ActiveVLA: Injecting active perception into vision-language-action models for precise 3D robotic manipula- tion,” 2026

    2026

  8. [16]

    VISO-grasp: Vision-language in- formed spatial object-centric 6-DoF active view planning and grasping in clutter and invisibility,

    Y . Shi, D. Wen, G. Chen,et al., “VISO-grasp: Vision-language in- formed spatial object-centric 6-DoF active view planning and grasping in clutter and invisibility,” in2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hangzhou, China: IEEE, Oct. ...

    2025

  9. [17]

    Active perception for grasp detec- tion via neural graspness field,

    B. Gao, D. Huang, H. Ma,et al., “Active perception for grasp detec- tion via neural graspness field,” inAdvances in Neural Information Processing Systems 37. Vancouver, BC, Canada: Neural Information Processing Systems Foundation, Inc. (NeurIPS), 2024, pp. 38 122– 38 141

    2024

  10. [18]

    GauSS-MI: Gaussian splatting shannon mutual information for active 3D reconstruction,

    Y . Xie, Y . Cai, Y . Zhang,et al., “GauSS-MI: Gaussian splatting shannon mutual information for active 3D reconstruction,” inRobotics: Science and Systems XXI. Robotics: Science and Systems Foundation, June 2025

    2025

  11. [19]

    Task-oriented grasp prediction with visual-language inputs,

    C. Tang, D. Huang, L. Meng,et al., “Task-oriented grasp prediction with visual-language inputs,” in2023 IEEE/RSJ International Confer- ence on Intelligent Robots and Systems (IROS). Detroit, MI, USA: IEEE, Oct. 2023, pp. 4881–4888

    2023

  12. [20]

    GLOVER: Generalizable open- vocabulary affordance reasoning for task-oriented grasping,

    T. Ma, Z. Wang, J. Zhou,et al., “GLOVER: Generalizable open- vocabulary affordance reasoning for task-oriented grasping,” 2024

    2024

  13. [21]

    Enhancing task-oriented robotic grasping via 3D affordance grounding from vision-language models,

    W. Chen, S.-C. Liu, Q. Li,et al., “Enhancing task-oriented robotic grasping via 3D affordance grounding from vision-language models,” Complex & Intelligent Systems, vol. 12, no. 1, pp. 42–56, Jan. 2026

    2026

  14. [22]

    NeRF: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik,et al., “NeRF: Representing scenes as neural radiance fields for view synthesis,”Communications of the ACM, vol. 65, no. 1, pp. 99–106, Jan. 2022

    2022

  15. [23]

    ESLAM: Efficient dense SLAM system based on hybrid representation of signed distance fields,

    M. M. Johari, C. Carta, and F. Fleuret, “ESLAM: Efficient dense SLAM system based on hybrid representation of signed distance fields,” in2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Vancouver, BC, Canada: IEEE, June 2023, pp. 17 408–17 419

    2023

  16. [24]

    Graspness discovery in clutters for fast and accurate grasp detection,

    C. Wang, H.-S. Fang, M. Gou,et al., “Graspness discovery in clutters for fast and accurate grasp detection,” in2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, QC, Canada: IEEE, Oct. 2021, pp. 15 944–15 953

    2021

  17. [25]

    FisherRF: Active view selection and mapping with radiance fields using fisher information,

    W. Jiang, B. Lei, and K. Daniilidis, “FisherRF: Active view selection and mapping with radiance fields using fisher information,” inCom- puter Vision – ECCV 2024, A. Leonardis, E. Ricci, S. Roth,et al., Eds. Cham: Springer Nature Switzerland, 2025, vol. 15071, pp. 422–440

    2024

  18. [26]

    Next best sense: Guiding vision and touch with FisherRF for 3D gaussian splatting,

    M. Strong, B. Lei, A. Swann,et al., “Next best sense: Guiding vision and touch with FisherRF for 3D gaussian splatting,” in2025 IEEE International Conference on Robotics and Automation (ICRA). Atlanta, GA, USA: IEEE, May 2025, pp. 3204–3210

    2025

  19. [27]

    3D AffordanceNet: A benchmark for visual object affordance understanding,

    S. Deng, X. Xu, C. Wu,et al., “3D AffordanceNet: A benchmark for visual object affordance understanding,” in2021 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR). Nashville, TN, USA: IEEE, June 2021, pp. 1778–1787

    2021

  20. [28]

    Superquadrics revisited: Learning 3D shape parsing beyond cuboids,

    D. Paschalidou, A. O. Ulusoy, and A. Geiger, “Superquadrics revisited: Learning 3D shape parsing beyond cuboids,” in2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, June 2019, pp. 10 336–10 345

    2019

  21. [29]

    Learning 6-DoF fine-grained grasp detection based on part affordance grounding,

    Y . Song, P. Sun, P. Jin,et al., “Learning 6-DoF fine-grained grasp detection based on part affordance grounding,”IEEE Transactions on Automation Science and Engineering, vol. 22, pp. 15 200–15 214, 2025

    2025

  22. [30]

    Contact-GraspNet: Efficient 6-DoF grasp generation in cluttered scenes,

    M. Sundermeyer, A. Mousavian, R. Triebel,et al., “Contact-GraspNet: Efficient 6-DoF grasp generation in cluttered scenes,” in2021 IEEE International Conference on Robotics and Automation (ICRA). Xi’an, China: IEEE, May 2021, pp. 13 438–13 444

    2021

  23. [31]

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

    C. R. Qi, L. Yi, H. Su,et al., “PointNet++: Deep hierarchical feature learning on point sets in a metric space,” inAdvances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc., 2017

    2017

  24. [32]

    Semi-supervised learning with graph learning-convolutional networks,

    B. Jiang, Z. Zhang, D. Lin,et al., “Semi-supervised learning with graph learning-convolutional networks,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 11 313–11 320

    2019

  25. [33]

    WordNet: A lexical database for english,

    G. A. Miller, “WordNet: A lexical database for english,”Communica- tions of the ACM, vol. 38, no. 11, pp. 39–41, Nov. 1995

    1995

  26. [34]

    A density-based algorithm for discovering clusters in large spatial databases with noise,

    M. Ester, H.-P. Kriegel, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” inKdd, vol. 96, 1996, pp. 226–231

    1996

  27. [35]

    Depth anything 3: Recovering the visual space from any views,

    H. Lin, S. Chen, J. Liew,et al., “Depth anything 3: Recovering the visual space from any views,” 2025

    2025

  28. [36]

    Grounded SAM: Assembling open- world models for diverse visual tasks,

    T. Ren, S. Liu, A. Zeng,et al., “Grounded SAM: Assembling open- world models for diverse visual tasks,” 2024

    2024

  29. [37]

    SAM 2: Segment anything in images and videos,

    N. Ravi, V . Gabeur, Y .-T. Hu,et al., “SAM 2: Segment anything in images and videos,” 2024

    2024

  30. [38]

    Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection,

    S. Liu, Z. Zeng, T. Ren,et al., “Grounding DINO: Marrying DINO with grounded pre-training for open-set object detection,” inComputer Vision – ECCV 2024, A. Leonardis, E. Ricci, S. Roth,et al., Eds. Cham: Springer Nature Switzerland, 2025, vol. 15105, pp. 38–55

    2024

  31. [39]

    3D gaussian splatting for real-time radiance field rendering,

    B. Kerbl, G. Kopanas, T. Leimkuehler,et al., “3D gaussian splatting for real-time radiance field rendering,”ACM Transactions on Graph- ics, vol. 42, no. 4, pp. 1–14, Aug. 2023

    2023

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Reviewed June 26, 2026 · model on record in the stance chip above.