Robust and fast generation of top and side grasps for unknown objects

Beatriz Leon; Brice Denoun; Claudio Zito; Lorenzo Jamone; Miles Hansard; Rustam Stolkin

arxiv: 1907.08088 · v1 · pith:TOYFVADKnew · submitted 2019-07-18 · 💻 cs.RO · cs.CV

Robust and fast generation of top and side grasps for unknown objects

Brice Denoun , Beatriz Leon , Claudio Zito , Rustam Stolkin , Lorenzo Jamone , Miles Hansard This is my paper

Pith reviewed 2026-05-24 19:47 UTC · model grok-4.3

classification 💻 cs.RO cs.CV

keywords graspingrobot manipulationRGB-D sensingunknown objectstop graspsside graspsgeometry-based planning

0 comments

The pith

A geometry-based algorithm generates both top and side grasps for unknown objects from one RGB-D view and selects the best one.

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

The paper presents a method that takes a single RGB-D image of an unknown object and quickly produces candidate top and side grasps based on the visible geometry, then ranks them to pick the most stable. This matters because many robot picking tasks must work on items never seen before and without extra sensing time or object models. The authors test the full pipeline on a real robot and report six times more successful grasps than a recent geometry-based baseline method.

Core claim

The algorithm uses surface normals and bounding-box geometry extracted from one RGB-D view to generate top grasps by aligning with the principal axes and side grasps by finding vertical support surfaces, then evaluates each candidate for stability and reachability to choose the highest-ranked grasp.

What carries the argument

A geometry-based grasping pipeline that extracts principal axes and support planes from a single RGB-D point cloud to produce and rank top and side grasp candidates.

If this is right

Robots can pick arbitrary objects in a single camera pass without building 3D models first.
Both overhead and lateral approaches become available from the same input data.
Grasp selection based on the same geometric features improves overall reliability in real picking trials.

Where Pith is reading between the lines

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

The same geometric features might be reused to plan collision-free approach paths before the grasp itself.
Combining the method with a second quick view from a different angle could further reduce failures on partially occluded objects.

Load-bearing premise

A single RGB-D view supplies enough geometric information to generate and select stable grasps for any unknown object without prior models or further sensing.

What would settle it

Running the method on a collection of objects whose graspable surfaces are hidden from one viewpoint and measuring whether the success rate falls below the sixfold improvement over the baseline.

read the original abstract

In this work, we present a geometry-based grasping algorithm that is capable of efficiently generating both top and side grasps for unknown objects, using a single view RGB-D camera, and of selecting the most promising one. We demonstrate the effectiveness of our approach on a picking scenario on a real robot platform. Our approach has shown to be more reliable than another recent geometry-based method considered as baseline [7] in terms of grasp stability, by increasing the successful grasp attempts by a factor of six.

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.

Desk Editor's Note private letter to a colleague

The paper gives a geometry-based way to generate top and side grasps from one RGB-D view of unknown objects and select between them, with a reported 6x gain in real-robot success over the cited baseline.

read the letter

The core of this work is a method that takes a single RGB-D image, produces candidate top and side grasps using geometric rules, and then picks the one it thinks will work best. They tested it on a real robot in a picking task and report six times more successful grasps than the baseline from reference [7]. That quantitative hardware result is the clearest thing the paper brings to the table. Extending the geometry approach to cover side grasps as well as top ones, plus adding an explicit selection step, is a straightforward but useful move for practical grasping pipelines. The fact that they closed the loop with physical trials rather than stopping at simulation is also a point in its favor. The main limitation is that the abstract supplies almost no information on the test objects, the number of trials, the range of shapes, or the precise success metric. Without those details it is hard to tell whether the improvement is general or tied to the particular objects and viewpoints used in the runs. The single-view premise itself could be brittle when stable grasp surfaces are occluded, and the paper would be stronger if it showed how the method behaves when that happens. The selection rule is mentioned but not unpacked enough to judge on its own. This is the kind of applied robotics paper that would interest people building pick-and-place systems for new objects with cheap sensors. It has enough of an experimental comparison to deserve peer review, though any referee would almost certainly ask for more data on the test conditions and failure cases.

Referee Report

2 major / 1 minor

Summary. The paper presents a geometry-based algorithm for generating top and side grasps for unknown objects from a single RGB-D view and selecting the most promising candidate. It reports a real-robot picking demonstration in which the method achieves a six-fold increase in successful grasp attempts relative to the geometry-based baseline of [7].

Significance. If the performance claims are substantiated, the work would provide a practical, model-free method for reliable single-view grasping of arbitrary objects, addressing a common need in robotic manipulation. The real-robot validation and direct comparison to an external baseline are strengths that support the utility of the approach.

major comments (2)

[Experiments] Experiments section: the central claim of a factor-of-six improvement in successful grasp attempts versus baseline [7] is presented without reporting the total number of trials, object set size or variety, breakdown by grasp type (top/side), viewpoint selection protocol, or any statistical measures such as standard deviation or confidence intervals. This information is load-bearing for evaluating whether the reliability gain is attributable to the algorithm rather than the specific test conditions.
[Method] Method description of grasp candidate generation and ranking: the geometric heuristics used to produce and select stable grasps from a partial point cloud are not accompanied by an analysis of failure modes when stable regions are occluded from the single viewpoint, which directly affects the premise that one RGB-D view suffices for arbitrary unknown objects.

minor comments (1)

[Figures] Figure captions and text should explicitly state the camera viewpoint relative to the object for each illustrated grasp example to allow readers to assess partial-view limitations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation of the experimental results and method limitations.

read point-by-point responses

Referee: [Experiments] Experiments section: the central claim of a factor-of-six improvement in successful grasp attempts versus baseline [7] is presented without reporting the total number of trials, object set size or variety, breakdown by grasp type (top/side), viewpoint selection protocol, or any statistical measures such as standard deviation or confidence intervals. This information is load-bearing for evaluating whether the reliability gain is attributable to the algorithm rather than the specific test conditions.

Authors: We agree that the experimental reporting lacks sufficient detail to allow full evaluation of the claims. In the revised manuscript we will expand the Experiments section to report the total number of trials performed, the size and variety of the object set, a breakdown of results by grasp type (top versus side), the protocol used for viewpoint selection, and statistical measures including standard deviations or confidence intervals computed from the trial data. revision: yes
Referee: [Method] Method description of grasp candidate generation and ranking: the geometric heuristics used to produce and select stable grasps from a partial point cloud are not accompanied by an analysis of failure modes when stable regions are occluded from the single viewpoint, which directly affects the premise that one RGB-D view suffices for arbitrary unknown objects.

Authors: The method is explicitly designed for single-view operation and the real-robot experiments demonstrate its effectiveness under that constraint. Nevertheless, we acknowledge that an explicit discussion of occlusion-related failure modes would improve the manuscript. We will add a dedicated paragraph analyzing potential failure cases when stable grasp regions are occluded, describing how the geometric heuristics and ranking step respond to partial observations, and noting the inherent limitations of the single-view premise. revision: yes

Circularity Check

0 steps flagged

No circularity: experimental claim rests on external baseline comparison

full rationale

The paper's central result is an empirical factor-of-six improvement in grasp success versus the external baseline [7], obtained from real-robot trials on a picking scenario. No equations, fitted parameters, or self-citations are presented that reduce the reported reliability gain to a definition or input by construction. The single-view RGB-D premise is an explicit modeling assumption tested experimentally rather than derived from the algorithm itself. This is the normal non-circular case for an applied robotics paper whose load-bearing evidence is external validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract only; no specific free parameters, axioms, or invented entities are identifiable from the provided information.

pith-pipeline@v0.9.0 · 5616 in / 1162 out tokens · 25502 ms · 2026-05-24T19:47:06.719694+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean reality_from_one_distinction unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Our approach has shown to be more reliable than another recent geometry-based method... by increasing the successful grasp attempts by a factor of six.
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We use Random Sample Consensus (RANSAC) to detect and fit the dominant plane... PCA... principal axes ui

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.