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REVIEW 2 minor 11 references

Reviewed by Pith at T0; open to challenge.

T0 means a machine referee read the full paper against a public rubric. The mark states how deep the mechanical check went, never who wrote it. the ladder, T0–T4 →

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T0 review · grok-4.3

An integrated pipeline with a multifunctional gripper and novel occlusion representations enables reliable sequential object picking in cluttered environments.

2026-06-27 06:40 UTC pith:X4CGS7EB

load-bearing objection A competition report on a second-place practical pipeline for sequential picking in clutter, with gripper tweaks and occlusion representations but thin on data or comparisons.

arxiv 2606.12954 v1 pith:X4CGS7EB submitted 2026-06-11 cs.RO

Towards Reliable Sequential Object Picking in Clutter: The Runner-up Solution to RGMC 2025

Wei Yu , Xidan Zhang , Ziyi Zheng , Weijie Kong , Huixu Dong This is my paper
classification cs.RO
keywords robotic graspingcluttered pickingsequential manipulationmultifunctional gripperocclusion representationrobotic competitiondecluttering strategies
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper introduces a hardware-software system for the demanding task of sequentially searching and picking objects from clutter. It tackles robust grasping of mixed rigid and deformable items while efficiently locating targets. The solution relies on a custom gripper and fresh ways to model how objects are distributed and occlude each other. Laboratory and competition tests show the approach works well enough to finish second in the Pick-in-Clutter track of RGMC 2025. Readers interested in practical robotics would care because few existing methods handle the full sequence of search and sort in real clutter.

Core claim

The authors claim their pipeline, built around a multifunctional gripper and representations of object distribution and occlusion relationships, delivers collision-aware grasping and efficient search, resulting in second place in the RGMC 2025 Pick-in-Clutter track.

What carries the argument

Multifunctional gripper hardware paired with representations of object distribution and occlusion relationships that support collision-aware planning and decluttering strategies.

Load-bearing premise

That the gripper design and the object distribution and occlusion representations will support robust performance across varied objects and clutter beyond the competition benchmark.

What would settle it

A test showing low grasping success or inefficient search on a new collection of objects with different shapes, materials, or occlusion patterns would indicate the claim does not hold generally.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The system achieves high success rates across rigid and deformable objects.
  • It enables efficient target search through decluttering in cluttered space.
  • Performance is validated in both controlled lab settings and the actual competition.
  • The approach handles sequential picking where prior methods were limited.

Where Pith is reading between the lines

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

  • Similar representations might improve grasping in other unstructured environments like warehouses.
  • Future work could test the pipeline on continuously changing clutter to check real-time adaptability.
  • The competition result suggests the design choices scale to contest-level benchmarks but may need tuning for broader use.

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

0 major / 2 minor

Summary. The manuscript presents an integrated hardware-software pipeline for sequential object picking in cluttered environments, featuring a multifunctional gripper design and novel representations for object distribution and occlusion relationships. It addresses challenges in robust collision-aware grasping and efficient search for rigid and deformable objects on the CEPB benchmark, claiming strong performance in lab tests and competition scenarios that resulted in second place in the Pick-in-Clutter track of RGMC 2025.

Significance. If the described pipeline performs as claimed, the work offers a practical, competition-validated system for industrial robotic manipulation tasks that remain difficult. The external falsifiable signal from the RGMC 2025 placement on a standardized benchmark is a clear strength, providing evidence of real-world applicability beyond internal simulations or controlled experiments.

minor comments (2)
  1. [Abstract] Abstract: the phrase 'novel representations for object distribution and occlusion relationships' is introduced without any definition, diagram, or pseudocode, leaving the core technical contribution opaque.
  2. [Abstract] Abstract: the assertion of 'strong performance in both laboratory tests and competition scenarios' is unsupported by any success rates, timing data, failure modes, or comparison to other entries.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, acknowledgment of the competition-validated performance on the standardized CEPB benchmark, and recommendation for minor revision. No specific major comments were provided in the report.

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is a competition report describing a hardware-software pipeline for robotic grasping. It contains no equations, derivations, fitted parameters, or predictions. The central claim rests on an external, falsifiable outcome (second place in RGMC 2025 Pick-in-Clutter track on the CEPB benchmark), which is independent of any internal construction. No self-citations, ansatzes, or self-definitional steps are present in the provided text. The derivation chain is therefore self-contained and non-circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review conducted on abstract only; no full text available to extract or evaluate free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5786 in / 967 out tokens · 14963 ms · 2026-06-27T06:40:13.205173+00:00 · methodology

0 comments
read the original abstract

As a long-standing challenge in robotic manipulation, stable and efficient grasping in cluttered environments is of great importance in industrial settings. While recent studies have achieved relatively high success rates in grasping from clutter, there remain few mature solutions for more demanding tasks such as sequential object search and sorting. This work addresses sequential object picking in cluttered environments based on the Cluttered Environment Picking Benchmark (CEPB) and presents our solution to the Pick-in-Clutter track of the 10th Robotic Grasping and Manipulation Competition (RGMC) at ICRA 2025. The task poses several key challenges. First, it requires robust and collision-aware grasping with high success rates across a diverse set of objects, including both rigid and deformable ones. Second, it demands efficient search for target objects, which places stringent requirements on the decluttering and searching strategies of the solution. To address the above challenges, we design an integrated hardware-software pipeline that combines object recognition, decluttering, and multi-modal grasping. The main contributions include the hardware design of a multifunctional gripper and novel representations for object distribution and occlusion relationships in cluttered space. This pipeline enables efficient recognition, search, and sequential grasping of objects in clutter, demonstrating strong performance in both laboratory tests and competition scenarios, and ultimately achieving second place in the Pick-in-Clutter track of the RGMC 2025.

Figures

Figures reproduced from arXiv: 2606.12954 by Huixu Dong, Weijie Kong, Wei Yu, Xidan Zhang, Ziyi Zheng.

Figure 1
Figure 1. Figure 1: Overview of our pipeline for sequential object picking from cluttered bins, which encompasses the object detection and segmentation process (top-left), rearragement policy (bottom) and grasp policy (top-right). I. INTRODUCTION As a long-standing challenge in robotic manipulation, stable and efficient grasping in cluttered environments is of great importance in industrial settings. While recent studies[1-5]… view at source ↗
Figure 2
Figure 2. Figure 2: Towards Reliable Sequential Object Picking in Clutter: The Runner-up Solution to RGMC 2025 Wei Yu1,*, Xidan Zhang1,*, Ziyi Zheng1,*, Weijie Kong1 and Huixu Dong1,† [PITH_FULL_IMAGE:figures/full_fig_p001_2.png] view at source ↗
Figure 2
Figure 2. Figure 2: Hardware setup consisting of the UR5e robotic arm, adaptive gripper, and wrist-mounted camera. B. Object Detection and Segmentation To achieve real-time detection and segmentation of indi￾vidual objects in clutter, we adopt NIDS-Net[7], a recently proposed framework for novel instance detection and seg￾mentation. NIDS-Net combines object proposal generation, embedding construction for both templates and ca… view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

11 extracted references · 6 canonical work pages · 4 internal anchors

  1. [1]

    Real-time robotic manipulation of cylindrical objects in dynamic scenarios through elliptic shape primitives,

    H. Dong, E. Asadi, G. Sun, D. K. Prasad and I.-M. Chen, “Real-time robotic manipulation of cylindrical objects in dynamic scenarios through elliptic shape primitives,” IEEE Transactions on Robotics, vol. 35, no. 1, pp. 95–113, Feb. 2019

    2019

  2. [2]

    Construction of a multiple-DOF underactuated gripper with force-sensing via deep learning,

    J. Li, K. Zhu, G. Lu, I.-M. Chen and H. Dong, “Construction of a multiple-DOF underactuated gripper with force-sensing via deep learning,” in Proc. Robotics: Science and Systems (RSS), Delft, Neth- erlands, Jul. 2024

    2024

  3. [3]

    SID: Sliding into Distribution for Robust Few-Demonstration Manipulation

    Y. Ma, W. Yu, Z. Su, X. Zhang and H. Dong, “SID: Sliding into Distribution for Robust Few-Demonstration Manipulation,” arXiv preprint arXiv:2605.13428, May 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  4. [4]

    Bin packing optimization via deep rein- forcement learning,

    B. Wang and H. Dong, “Bin packing optimization via deep rein- forcement learning,” arXiv preprint arXiv:2403.12420, 2024

    work page arXiv 2024

  5. [5]

    Multimodal grasp planner for hybrid grippers in cluttered scenes,

    S. D’Avella, A. M. Sundaram, W. Friedl, P. Tripicchio and M. A. Roa, “Multimodal grasp planner for hybrid grippers in cluttered scenes,” IEEE Robotics and Automation Letters, vol. 8, no. 4, pp. 2030–2037, Apr. 2023

    2030

  6. [6]

    The Cluttered Environment Picking Benchmark (CEPB) for Advanced Warehouse Automation: Evaluating the Per- ception, Planning, Control, and Grasping of Manipulation Systems,

    S. D’Avella, M. Bianchi, A. M. Sundaram, C. A. Avizzano, M. A. Roa, and P. Tripicchio, “The Cluttered Environment Picking Benchmark (CEPB) for Advanced Warehouse Automation: Evaluating the Per- ception, Planning, Control, and Grasping of Manipulation Systems,” IEEE Robotics & Automation Magazine, vol. 31, no. 4, pp. 45–58, Dec. 2024

    2024

  7. [7]

    Adapting Pre-Trained Vision Models for Novel Instance Detection and Seg- mentation,

    Y. Lu, J. J. P. Jaykumar, Y. Guo, N. Ruozzi, and Y. Xiang, “Adapting Pre-Trained Vision Models for Novel Instance Detection and Seg- mentation,” arXiv preprint arXiv:2405.17859, 2024

    work page arXiv 2024

  8. [8]

    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, C. Li, J. Yang, H. Su, J. Zhu, et al., “Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection,” arXiv preprint arXiv:2303.05499, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  9. [9]

    Segment Anything

    A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment Anything,” arXiv:2304.02643, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  10. [10]

    DINOv2: Learning Robust Visual Features without Supervision

    M. Oquab, T. Darcet, T. Moutakanni, H. V. Vo, M. Szafraniec, V. Khalidov, P. Fernandez, D. Haziza, F. Massa, A. El-Nouby, R. Howes, P.-Y. Huang, H. Xu, V. Sharma, S.-W. Li, W. Galuba, M. Rabbat, M. Assran, N. Ballas, G. Synnaeve, I. Misra, H. Jegou, J. Mairal, P. Labatut, A. Joulin, and P. Bojanowski, “DINOv2: Learning Robust Visual Features without Super...

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  11. [11]

    AnyGrasp: Robust and Efficient Grasp Perception in Spa- tial 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 Spa- tial and Temporal Domains,” IEEE Transactions on Robotics (T-RO), 2023

    2023