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arxiv: 2603.07866 · v2 · submitted 2026-03-09 · 💻 cs.RO · cs.LG· cs.SY· eess.SY

Viewpoint-Agnostic Grasp Pipeline using VLM and Partial Observations

Dilermando Almeida , Juliano Negri , Guilherme Lazzarini , Thiago H. Segreto , Ranulfo Bezerra , Ricardo V. Godoy , Marcelo Becker This is my paper

Pith reviewed 2026-05-15 15:35 UTC · model grok-4.3

classification 💻 cs.RO cs.LGcs.SYeess.SY
keywords robotic graspingpoint cloud completionopen-vocabulary detectionpartial observationscluttered environments6-DoF graspsvision-language modelslegged manipulators
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The pith

A language-guided pipeline completes partial point clouds to select safe grasps despite occlusions in clutter.

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

The paper builds an end-to-end system that takes a natural-language command, locates the target object in an RGB image with open-vocabulary detection and promptable segmentation, and extracts an object-centric point cloud from RGB-D data. It then applies back-projected depth compensation and two-stage completion to reconstruct missing geometry caused by occlusions, generates 6-DoF grasp candidates, and filters them with safety heuristics for reachability and clearance. The system is demonstrated on a quadruped robot with an arm in two real cluttered tabletop setups, where paired trials show higher reliability than a view-dependent baseline. A sympathetic reader would care because most real-world scenes hide parts of objects from any single viewpoint, and reliable grasping without full visibility is a practical barrier to deploying robots in homes or warehouses.

Core claim

The central claim is that grounding language commands via open-vocabulary detection and promptable segmentation, followed by depth compensation and two-stage point cloud completion, produces object geometry accurate enough for collision-aware 6-DoF grasp generation and heuristic selection of executable grasps, even when the initial observations are partial and unreliable.

What carries the argument

Two-stage point cloud completion applied after back-projected depth compensation on object-centric RGB-D crops, which reconstructs occluded geometry to support grasp candidate generation and safety filtering.

If this is right

  • Natural-language commands can specify grasp targets without requiring pre-built object models or databases.
  • The pipeline supports mobile legged manipulators operating directly in unstructured, cluttered tabletop scenes.
  • Safety heuristics that incorporate reachability, approach direction, and clearance produce executable grasps from the completed geometry.
  • Handling partial observations through completion yields measurably higher reliability than methods that rely on the initial incomplete view.

Where Pith is reading between the lines

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

  • The same completion step could be applied to non-tabletop settings such as shelf or floor picking where viewpoints are even more constrained.
  • Pairing the pipeline with a task-level planner would allow handling compound instructions that require multiple sequential grasps.
  • Replacing the fixed completion model with one fine-tuned on robot-specific failure data might further reduce collisions in novel clutter arrangements.

Load-bearing premise

That open-vocabulary detection, promptable segmentation, and two-stage point cloud completion will reliably recover accurate enough object geometry from partial RGB-D views to avoid collisions with hidden surfaces during grasp execution.

What would settle it

A trial in which the completed point cloud produces a grasp pose that collides with an unseen portion of the target or surrounding clutter, causing physical failure on the robot despite successful planning.

Figures

Figures reproduced from arXiv: 2603.07866 by Dilermando Almeida, Guilherme Lazzarini, Juliano Negri, Marcelo Becker, Ranulfo Bezerra, Ricardo V. Godoy, Thiago H. Segreto.

Figure 1
Figure 1. Figure 1: A legged mobile manipulator performs language [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: System overview of the proposed viewpoint-agnostic grasping pipeline. The system receives a natural-language target [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Spot front-left registered stereo and RGB images [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Experimental setups for evaluating the viewpoint [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Sequence demonstrating the grasp execution experiments using the proposed end-to-end pipeline on the real robot. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
read the original abstract

Robust grasping in cluttered, unstructured environments remains challenging for mobile legged manipulators due to occlusions that lead to partial observations, unreliable depth estimates, and the need for collision-free, execution-feasible approaches. In this paper we present an end-to-end pipeline for language-guided grasping that bridges open-vocabulary target selection to safe grasp execution on a real robot. Given a natural-language command, the system grounds the target in RGB using open-vocabulary detection and promptable instance segmentation, extracts an object-centric point cloud from RGB-D, and improves geometric reliability under occlusion via back-projected depth compensation and two-stage point cloud completion. We then generate and collision-filter 6-DoF grasp candidates and select an executable grasp using safety-oriented heuristics that account for reachability, approach feasibility, and clearance. We evaluate the method on a quadruped robot with an arm in two cluttered tabletop scenarios, using paired trials against a view-dependent baseline. The proposed approach achieves a 90% overall success rate (9/10) against 30% (3/10) for the baseline, demonstrating substantially improved robustness to occlusions and partial observations in clutter.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The paper presents an end-to-end pipeline for language-guided grasping on a quadruped robot that uses open-vocabulary detection and promptable segmentation from VLMs to ground targets in RGB, extracts object-centric point clouds from RGB-D, applies back-projected depth compensation and two-stage point cloud completion to handle occlusions, generates collision-filtered 6-DoF grasp candidates, and selects executable grasps using safety heuristics. In two cluttered tabletop scenarios, it reports 90% success (9/10 trials) versus 30% (3/10) for a view-dependent baseline.

Significance. If the empirical results hold under more rigorous testing, the pipeline could significantly improve robustness of grasping in unstructured, occluded environments for mobile manipulators by integrating VLM-based perception with geometric completion and safety-aware planning.

major comments (1)
  1. [Evaluation] Evaluation section: The headline result of 90% (9/10) versus 30% (3/10) success is drawn from only 10 paired trials per condition across two scenarios, with no variance estimates, confidence intervals, randomization protocol, or statistical tests reported. This small-N design leaves the observed gap compatible with sampling noise or scenario-specific effects, so it cannot reliably support the abstract's claim of substantially improved robustness to occlusions and partial observations.
minor comments (1)
  1. [Abstract] Abstract: The description of the two tabletop scenarios lacks detail on object types, clutter density, or occlusion levels, making it hard to judge the generality of the reported performance.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on our evaluation methodology. We agree that the current reporting lacks statistical rigor and will revise the manuscript accordingly to strengthen the presentation of results while acknowledging practical constraints of real-robot experiments.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: The headline result of 90% (9/10) versus 30% (3/10) success is drawn from only 10 paired trials per condition across two scenarios, with no variance estimates, confidence intervals, randomization protocol, or statistical tests reported. This small-N design leaves the observed gap compatible with sampling noise or scenario-specific effects, so it cannot reliably support the abstract's claim of substantially improved robustness to occlusions and partial observations.

    Authors: We acknowledge that the sample size of 10 trials per condition is small and that the absence of variance estimates, confidence intervals, and explicit randomization details weakens the statistical support for our claims. In the revised manuscript we will: (1) describe the trial randomization protocol (random ordering of conditions within each scenario and balanced scenario presentation), (2) report bootstrap 95% confidence intervals for the observed success rates, and (3) add an explicit limitations subsection discussing the implications of small-N real-robot evaluation. We will also moderate the abstract and conclusion language to frame the results as a proof-of-concept demonstration rather than a statistically conclusive claim. Due to hardware and time constraints we cannot expand the trial count in this revision, but the paired design across identical scenes provides some control for scenario-specific effects. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical pipeline with direct trial comparison

full rationale

The paper describes an engineering pipeline (open-vocabulary detection, segmentation, point-cloud completion, grasp generation) and reports success rates from 10 paired robot trials against a baseline. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the provided text. The central claim reduces to measured performance in physical experiments rather than any derivation that collapses to its own assumptions by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The pipeline depends on the reliability of off-the-shelf VLM and segmentation models plus geometric completion methods; no new entities or fitted parameters are introduced in the abstract.

axioms (2)
  • domain assumption Open-vocabulary detection and promptable instance segmentation reliably ground natural-language targets in RGB under occlusion
    Foundation of the target selection step
  • domain assumption Back-projected depth compensation and two-stage point cloud completion produce sufficiently accurate geometry for grasp planning
    Central to handling partial observations

pith-pipeline@v0.9.0 · 5536 in / 1372 out tokens · 45311 ms · 2026-05-15T15:35:22.364721+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

28 extracted references · 28 canonical work pages

  1. [1]

    Ensuring robot- human safety for the bd spot using active visual tracking and nmpc with velocity obstacles,

    S. Karlsson, B. Lindqvist, and G. Nikolakopulos, “Ensuring robot- human safety for the bd spot using active visual tracking and nmpc with velocity obstacles,”IEEE Access, vol. 10, 2022

    work page 2022

  2. [2]

    Construction inspection & monitoring with quadruped robots in future human-robot teaming: A preliminary study,

    S. Halder, K. Afsari, E. Chiou, R. Patrick, and K. A. Hamed, “Construction inspection & monitoring with quadruped robots in future human-robot teaming: A preliminary study,”Journal of Building Engineering, vol. 65, p. 105814, 2023

    work page 2023

  3. [3]

    Automation and robotics in the context of industry 4.0: the shift to collaborative robots,

    R. Galin and R. Meshcheryakov, “Automation and robotics in the context of industry 4.0: the shift to collaborative robots,” inIOP Conference Series: Materials Science and Engineering, 2019

    work page 2019

  4. [4]

    6-dof grasping for target-driven object manipulation in clutter,

    A. Murali, A. Mousavian, C. Eppner, C. Paxton, and D. Fox, “6-dof grasping for target-driven object manipulation in clutter,” in2020 IEEE International Conference on Robotics and Automation (ICRA), 2020

    work page 2020

  5. [5]

    Icgnet: A unified approach for instance-centric grasping,

    R. Zurbr ¨ugg, Y . Liu, F. Engelmann, S. Kumar, M. Hutter, V . Patil, and F. Yu, “Icgnet: A unified approach for instance-centric grasping,” in2024 IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 4140–4146

    work page 2024

  6. [6]

    Ge-grasp: Efficient target-oriented grasping in dense clutter,

    Z. Liu, Z. Wang, S. Huang, J. Zhou, and J. Lu, “Ge-grasp: Efficient target-oriented grasping in dense clutter,” in2022 IEEE/RSJ Interna- tional Conference on Intelligent Robots and Systems (IROS), 2022

    work page 2022

  7. [7]

    Data-driven grasp synthesis—a survey,

    J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis—a survey,”IEEE Transactions on robotics, 2013

    work page 2013

  8. [8]

    Synergies between affordance and geometry: 6-dof grasp detection via implicit representa- tions,

    Z. Jiang, Y . Zhu, M. Svetlik, K. Fang, and Y . Zhu, “Synergies between affordance and geometry: 6-dof grasp detection via implicit representations,”arXiv preprint arXiv:2104.01542, 2021

    work page arXiv 2021

  9. [9]

    Learning visual quadrupedal loco-manipulation from demonstrations,

    Z. He, K. Lei, Y . Ze, K. Sreenath, Z. Li, and H. Xu, “Learning visual quadrupedal loco-manipulation from demonstrations,” in2024 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, 2024, pp. 9102–9109

    work page 2024

  10. [10]

    Whole-body control loco- manipulation strategy for quadruped robots on deformable terrains,

    C. Wang, O. K. Adak, and R. Fuentes, “Whole-body control loco- manipulation strategy for quadruped robots on deformable terrains,” in 2024 IEEE International Conference on Advanced Intelligent Mecha- tronics (AIM). IEEE, 2024, pp. 886–891

    work page 2024

  11. [11]

    Roloma: Robust loco-manipulation for quadruped robots with arms,

    H. Ferrolho, V . Ivan, W. Merkt, I. Havoutis, and S. Vijayakumar, “Roloma: Robust loco-manipulation for quadruped robots with arms,” Autonomous Robots, vol. 47, no. 8, pp. 1463–1481, 2023

    work page 2023

  12. [12]

    Hierarchical optimization-based control for whole-body loco-manipulation of heavy objects,

    A. Rigo, M. Hu, S. K. Gupta, and Q. Nguyen, “Hierarchical optimization-based control for whole-body loco-manipulation of heavy objects,” in2024 IEEE International Conference on Robotics and Automation (ICRA), 2024, pp. 15 322–15 328

    work page 2024

  13. [13]

    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

    work page 2017

  14. [14]

    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,” in2021 IEEE international conference on robotics and automation (ICRA). IEEE, 2021, pp. 13 438–13 444

    work page 2021

  15. [15]

    Optimizing grasping in legged robots: A deep learning approach to loco-manipulation,

    D. Almeida, G. Lazzarini, J. Negri, T. H. Segreto, R. V . Godoy, and M. Becker, “Optimizing grasping in legged robots: A deep learning approach to loco-manipulation,” in2025 Latin American Robotics Symposium (LARS), 2025, pp. 1–6

    work page 2025

  16. [16]

    Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,

    H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y . Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,”IEEE Transactions on Robotics, 2023

    work page 2023

  17. [17]

    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,” in2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016

    work page 2016

  18. [18]

    Graspsplats: Efficient ma- nipulation with 3d feature splatting,

    M. Ji, R.-Z. Qiu, X. Zou, and X. Wang, “Graspsplats: Efficient ma- nipulation with 3d feature splatting,”arXiv preprint arXiv:2409.02084, 2024

    work page arXiv 2024

  19. [19]

    Robogsim: A real2sim2real robotic gaussian splatting simulator, 2025

    X. Li, J. Li, Z. Zhang, R. Zhang, F. Jia, T. Wang, H. Fan, K.-K. Tseng, and R. Wang, “Robogsim: A real2sim2real robotic gaussian splatting simulator,”arXiv preprint arXiv:2411.11839, 2024

    work page arXiv 2024

  20. [20]

    Open-vocabulary part-based grasping.arXiv preprint arXiv:2406.05951, 2024

    T. van Oort, D. Miller, W. N. Browne, N. Marticorena, J. Haviland, and N. Suenderhauf, “Open-vocabulary part-based grasping,”arXiv preprint arXiv:2406.05951, 2024

    work page arXiv 2024

  21. [21]

    Affordgrasp: In-context affordance reasoning for open-vocabulary task-oriented grasping in clutter,

    Y . Tang, S. Zhang, X. Hao, P. Wang, J. Wu, Z. Wang, and S. Zhang, “Affordgrasp: In-context affordance reasoning for open-vocabulary task-oriented grasping in clutter,” in2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2025

    work page 2025

  22. [22]

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

    S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, Q. Jiang, C. Li, J. Yang, H. Suet al., “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” inEuropean conference on computer vision. Springer, 2024, pp. 38–55

    work page 2024

  23. [23]

    Sam 2: Segment anything in images and videos,

    N. Ravi, V . Gabeur, Y .-T. Hu, R. Hu, C. Ryali, T. Ma, H. Khedr, R. R ¨adle, C. Rolland, L. Gustafsonet al., “Sam 2: Segment anything in images and videos,” inThe Thirteenth International Conference on Learning Representations, 2024

    work page 2024

  24. [24]

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

    T. Ren, S. Liu, A. Zeng, J. Lin, K. Li, H. Cao, J. Chen, X. Huang, Y . Chen, F. Yan, Z. Zeng, H. Zhang, F. Li, J. Yang, H. Li, Q. Jiang, and L. Zhang, “Grounded sam: Assembling open-world models for diverse visual tasks,” 2024

    work page 2024

  25. [25]

    nvblox: Gpu-accelerated incremental signed distance field mapping,

    A. Millane, H. Oleynikova, E. Wirbel, R. Steiner, V . Ramasamy, D. Tingdahl, and R. Siegwart, “nvblox: Gpu-accelerated incremental signed distance field mapping,” in2024 IEEE International Confer- ence on Robotics and Automation (ICRA). IEEE, 2024

    work page 2024

  26. [26]

    Mgpc: Multi- modal network for generalizable point cloud completion with modality dropout and progressive decoding,

    J. Liu, Y . Zhao, H. Ma, Z. Liu, J. Wang, and W. Zou, “Mgpc: Multi- modal network for generalizable point cloud completion with modality dropout and progressive decoding,”arXiv preprint arXiv:2601.03660, 2026

    work page arXiv 2026

  27. [27]

    Pointr: Diverse point cloud completion with geometry-aware transformers,

    X. Yu, Y . Rao, Z. Wang, Z. Liu, J. Lu, and J. Zhou, “Pointr: Diverse point cloud completion with geometry-aware transformers,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 12 498–12 507

    work page 2021

  28. [28]

    Ros: an open-source robot operating system,

    M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y . Nget al., “Ros: an open-source robot operating system,” inICRA workshop on open source software, vol. 3, no. 3.2. Kobe, 2009, p. 5

    work page 2009