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arxiv: 2605.02135 · v1 · submitted 2026-05-04 · 💻 cs.RO

Robotic Desk Organization: A Multi-Primitive Approach to Manipulating Heterogeneous Objects via Environmental Constraints

Yi Dong. Yangjun Liu , Jinjun Duan , Yang Li , Zhendong Dai This is my paper

Pith reviewed 2026-05-08 18:27 UTC · model grok-4.3

classification 💻 cs.RO
keywords robotic manipulationdesk organizationgrasping primitivesenvironmental constraintsperception pipelineplanar objectstask planning
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The pith

Robots can organize mixed rigid and deformable desk objects by detecting and using table edges to guide grasping actions.

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

The paper is trying to establish that robots can effectively organize desks with varied objects by detecting and using environmental constraints such as table edges to guide their grasping actions. A sympathetic reader would care because this could bring practical home robots closer to reality by avoiding the need for perfect object recognition or specialized tools for every item. The approach develops specific primitives for rigid and deformable planar objects and combines them with a planner for complete tasks. Sufficient experiments show the framework works robustly in real conditions.

Core claim

The central claim is that by developing a perception pipeline capable of estimating poses and keypoints for uncommon desktop items and detecting environmental constraints, and by employing environment-assisted primitives such as contact-based grasping, edge-based push-grasping for rigid objects, and levering-based grasping for deformable objects, along with a task planner, robots can perform complex multi-object organization tasks on desks, as demonstrated by real-world experiments.

What carries the argument

Environment-assisted primitives that use table edges and inter-object contacts for grasping planar rigid and deformable objects.

Load-bearing premise

The perception pipeline can reliably detect objects, keypoints, and environmental constraints such as table edges under typical desktop lighting and clutter.

What would settle it

Real-world trials in which the perception system fails to detect table edges or keypoints in standard indoor lighting with moderate clutter, resulting in misaligned or failed grasps.

Figures

Figures reproduced from arXiv: 2605.02135 by Jinjun Duan, Yang Li, Yi Dong. Yangjun Liu, Zhendong Dai.

Figure 1
Figure 1. Figure 1: Initial and final states of the desktop organization t view at source ↗
Figure 2
Figure 2. Figure 2: Perception pipeline for desktop organization. The R view at source ↗
Figure 4
Figure 4. Figure 4: Push-grasping primitive for retrieving a ruler from view at source ↗
Figure 5
Figure 5. Figure 5: Pry-grasping primitive. (1) Contact phase; (2) Pryi view at source ↗
Figure 7
Figure 7. Figure 7: Objects used in primitive experiments. Red dashed bo view at source ↗
Figure 6
Figure 6. Figure 6: Workspace and gripping force of the finger. (a) Grippe view at source ↗
Figure 8
Figure 8. Figure 8: Success rate of grasp-and-place for small objects. ( view at source ↗
Figure 10
Figure 10. Figure 10: Organization experiments with planar deformable o view at source ↗
Figure 11
Figure 11. Figure 11: Book initial state and pry-grasping failure analys view at source ↗
Figure 9
Figure 9. Figure 9: Success rate of grasp-and-place for rulers on differ view at source ↗
Figure 12
Figure 12. Figure 12: Overall task success rate and analysis for desktop o view at source ↗
read the original abstract

Desktop organization remains challenging for service robots because of heterogeneous objects and diverse manipulation objectives, such as collection and stacking. In this article, a task-oriented framework is presented for organizing planar rigid and deformable objects on desks. A perception pipeline was developed that augments existing datasets with uncommon desktop items and makes geometry-based pose and keypoint estimation possible, along with the detection of environmental constraints, such as table edges. To handle diverse manipulation requirements, environment-assisted primitives are used, including contact-based grasping for small objects, edge-based push-grasping for planar rigid objects, and levering-based grasping for planar deformable objects. These primitives leverage environmental and interobject constraints to improve robustness. A task planner was designed to integrate these primitives into multiobject organization. Sufficient real-world experiments demonstrate the effectiveness and robustness of the proposed framework. This research provides practical manipulation primitives for planar rigid and deformable objects, highlighting the role of environmental and interobject constraints in complex multiobject manipulation tasks. Code and video are available online.

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

2 major / 1 minor

Summary. The paper presents a task-oriented framework for organizing planar rigid and deformable objects on desks using a perception pipeline that augments datasets for uncommon items to enable geometry-based pose/keypoint estimation and environmental constraint detection (e.g., table edges). It introduces environment-assisted manipulation primitives—contact-based grasping for small objects, edge-based push-grasping for rigid planar objects, and levering-based grasping for deformable objects—that exploit environmental and inter-object constraints, integrated via a task planner for multi-object tasks. The central claim is that sufficient real-world experiments demonstrate the effectiveness and robustness of this multi-primitive approach.

Significance. If the experimental results hold under scrutiny, the work is significant for service robotics by providing practical, constraint-leveraging primitives that improve robustness for heterogeneous desktop objects without requiring advanced sensing or precise control. The availability of code and video is a clear strength supporting reproducibility.

major comments (2)
  1. Abstract: The central claim that 'sufficient real-world experiments demonstrate the effectiveness and robustness of the proposed framework' is load-bearing but unsupported by any quantitative metrics (success rates, precision/recall for perception, failure modes, baselines, or statistical significance), leaving the robustness of the perception pipeline and primitives unverifiable under variable lighting or clutter.
  2. Perception pipeline description: No performance evaluation is provided for object detection, keypoint estimation, or environmental constraint detection (e.g., table edges) on augmented datasets with uncommon items, which is critical because the contact-based, edge-push, and levering primitives cannot be invoked without reliable perception.
minor comments (1)
  1. The abstract could more explicitly state the number of trials, object categories tested, and specific success criteria to strengthen the experimental claim.

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 and indicate planned revisions to strengthen the quantitative support for our claims.

read point-by-point responses

  1. Referee: Abstract: The central claim that 'sufficient real-world experiments demonstrate the effectiveness and robustness of the proposed framework' is load-bearing but unsupported by any quantitative metrics (success rates, precision/recall for perception, failure modes, baselines, or statistical significance), leaving the robustness of the perception pipeline and primitives unverifiable under variable lighting or clutter.

    Authors: We agree that the abstract claim would be more robust with explicit quantitative metrics. The current manuscript supports the claim through detailed descriptions of real-world trials and video demonstrations rather than tabulated success rates or statistical tests. In revision, we will add a quantitative results subsection reporting success rates for each primitive, perception accuracy metrics on the augmented dataset, failure mode categorization, and any available baseline comparisons to make the robustness verifiable. revision: yes

  2. Referee: Perception pipeline description: No performance evaluation is provided for object detection, keypoint estimation, or environmental constraint detection (e.g., table edges) on augmented datasets with uncommon items, which is critical because the contact-based, edge-push, and levering primitives cannot be invoked without reliable perception.

    Authors: The referee correctly notes the lack of numerical performance metrics for the perception components. The manuscript describes the dataset augmentation process and geometry-based methods but does not report precision, recall, or accuracy figures. We will revise the perception section to include these evaluations, such as detection and keypoint estimation results on the augmented uncommon-item dataset and constraint detection accuracy, thereby confirming the pipeline's reliability for invoking the manipulation primitives. revision: yes

Circularity Check

0 steps flagged

No circularity: engineering framework validated on external hardware

full rationale

The paper describes a perception pipeline, environment-assisted manipulation primitives (contact-based grasping, edge-push, levering), and a task planner, all presented as engineering choices rather than derived from equations or fitted parameters. Effectiveness is asserted via real-world robot experiments on physical hardware and objects, which serve as independent external benchmarks. No mathematical derivations, self-citations forming load-bearing chains, or reductions of claims to inputs by construction appear in the provided text. The work is therefore self-contained against external validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work relies on standard robotics assumptions such as rigid-body dynamics for planar objects and reliable RGB-D sensing; no new free parameters, axioms, or invented entities are introduced beyond conventional computer-vision and control techniques.

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discussion (0)

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Reference graph

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