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arxiv: 1907.03835 · v1 · pith:455BCV56new · submitted 2019-07-08 · 💻 cs.RO

Assembly Planning by Subassembly Decomposition Using Blocking Reduction

James Watson , Tucker Hermans This is my paper

Pith reviewed 2026-05-25 00:57 UTC · model grok-4.3

classification 💻 cs.RO
keywords assembly sequence planningdisassembly interference graphsubassembly decompositionpart blockage measureparallel assemblyroboticsobstruction modeling
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The pith

Obstruction relationships between parts can be captured as a disassembly interference graph to generate efficient, parallelizable assembly sequences by minimizing a part blockage measure.

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 to model how parts obstruct each other in an assembly using a graph called the disassembly interference graph. This graph guides the decomposition of the full assembly into smaller subassemblies, producing a tree that shows where parallel assembly steps are possible. The planner selects sequences that reduce a blockage score, and the resulting plans have lengths comparable to existing state-of-the-art approaches. A reader would care because shorter, more parallel sequences can reduce time and effort when humans or robots put complex products together.

Core claim

The central claim is that expressing obstruction relationships as a disassembly interference graph allows a planner to generate successive subassembly decompositions that minimize part blockage, yielding viable disassembly plans whose total length matches that of two existing assembly methods while also making parallelization opportunities explicit in the resulting tree structure.

What carries the argument

The disassembly interference graph (DIG), which encodes pairwise obstruction relationships between parts and is used to compute and minimize a part blockage measure during subassembly decomposition.

If this is right

  • The method produces a tree of subassemblies that explicitly reveals opportunities for parallel execution.
  • Plans generated by minimizing the blockage measure achieve total lengths comparable to two state-of-the-art assembly planners.
  • The part blockage measure is shown to be a useful addition to the existing Assembly Sequence Planning toolkit.
  • Successive decompositions guided by the graph yield viable disassembly sequences for the tested assemblies.

Where Pith is reading between the lines

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

  • The tree structure could be used directly by multi-robot systems to assign subassemblies to different agents for simultaneous work.
  • The blockage measure might be combined with stability or grasp-quality checks to produce plans that are both short and physically reliable.
  • Because the graph is built from pairwise obstructions, it could be updated incrementally when a single part is redesigned.

Load-bearing premise

That the disassembly interference graph accurately captures real obstruction relationships in physical assemblies and that minimizing the part blockage measure produces viable and efficient plans without needing extra constraints or adjustments.

What would settle it

Run the planner on a physical multi-part assembly whose real-world disassembly requires a longer sequence than the generated plan or encounters an obstruction the graph did not predict.

Figures

Figures reproduced from arXiv: 1907.03835 by James Watson, Tucker Hermans.

Figure 1
Figure 1. Figure 1: A cube rests in a notch cut from a cuboid plate. The [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Removal space of the cube shown in Figure 1, [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Intersection surfaces, Spherical Pyramid. [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Part A cannot be removed alone, but a subassembly consisting of A and B can. most-blocking part is then popped from the queue, and the process repeats on parts that have neither been evaluated nor assigned to another subassembly. Once the queue is emptied, the subassembly accumulation process is repeated on the nu￾cleus part with the next-highest score. The identification phase ends when all nuclei and the… view at source ↗
Figure 6
Figure 6. Figure 6: Motor Driver test case, from Wang, Liu, and Zhong [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 5
Figure 5. Figure 5: Module Box test case, created for this experiment [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Blocking Reduction assembly plan for the Module Box. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 9
Figure 9. Figure 9: Belhadj assembly plan for the Module Box. [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Blocking Reduction assembly plan for the Motor [PITH_FULL_IMAGE:figures/full_fig_p007_10.png] view at source ↗
Figure 8
Figure 8. Figure 8: Morato assembly plan for the Module Box. [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 11
Figure 11. Figure 11: Morato assembly plan for the Motor Driver [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Belhadj assembly plan for the Motor Driver [PITH_FULL_IMAGE:figures/full_fig_p008_12.png] view at source ↗
read the original abstract

The sequence in which a complex product is assembled directly impacts the ease and efficiency of the assembly process, whether executed by a human or a robot. A sequence that gives the assembler the greatest freedom of movement is therefore desirable. Our main contribution is an expression of obstruction relationships between parts as a disassembly interference graph (DIG). We validate this heuristic by developing a disassembly sequence planner that partitions assemblies in a way that prioritizes access to parts, resulting in plans that are comparable in efficiency to two state-of-the-art assembly methods in terms of total plan length. Using DIG, our method generates successive subassembly decompositions, yielding a tree structure that makes parallization opportunities apparent. Our planner generates viable disassembly plans by minimizing our part blockage measure, and thereby demonstrates that this measure is a valuable addition to the Assembly Sequence Planning toolkit.

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 / 0 minor

Summary. The paper introduces a Disassembly Interference Graph (DIG) to express obstruction relationships between parts in an assembly and develops a planner that generates successive subassembly decompositions by minimizing a part blockage measure. It claims the resulting disassembly plans are viable, comparable in total length to two state-of-the-art methods, and yield a tree structure that makes parallelization opportunities explicit, thereby demonstrating the value of the blockage measure to the assembly sequence planning toolkit.

Significance. If the DIG construction and blockage minimization are shown to produce physically executable plans without unmodeled constraints, the approach could provide a useful heuristic that explicitly surfaces parallelization via the decomposition tree. The explicit comparison to external SOTA methods on plan length is a positive element of the validation strategy.

major comments (2)
  1. [Abstract] Abstract: the claim that the method 'produces comparable plan lengths' and 'generates viable disassembly plans' is asserted without any description of test assemblies, how interferences are computed from geometry to populate the DIG, the two SOTA baselines, or any validation (e.g., physics simulation) that the generated sequences are executable; this directly undermines the central claim that the blockage measure is a valuable addition to the toolkit.
  2. [Abstract] Abstract: the assertion that 'minimizing our part blockage measure' yields viable plans assumes the DIG encodes all relevant physical obstructions and that no additional feasibility constraints (stability, tool access, non-pairwise interferences) are required, yet the manuscript provides no evidence or discussion addressing this assumption.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We address the two major comments on the abstract point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the method 'produces comparable plan lengths' and 'generates viable disassembly plans' is asserted without any description of test assemblies, how interferences are computed from geometry to populate the DIG, the two SOTA baselines, or any validation (e.g., physics simulation) that the generated sequences are executable; this directly undermines the central claim that the blockage measure is a valuable addition to the toolkit.

    Authors: Abstracts are concise summaries and cannot contain full methodological details. The test assemblies, geometry-to-DIG interference computation, the two SOTA baselines, and the plan-length comparisons establishing viability are all described in the body of the manuscript. The abstract therefore states the claims at the appropriate level of abstraction; the supporting evidence appears in the main text rather than being omitted. revision: no

  2. Referee: [Abstract] Abstract: the assertion that 'minimizing our part blockage measure' yields viable plans assumes the DIG encodes all relevant physical obstructions and that no additional feasibility constraints (stability, tool access, non-pairwise interferences) are required, yet the manuscript provides no evidence or discussion addressing this assumption.

    Authors: The manuscript presents the DIG as a model of pairwise obstruction relationships and demonstrates that plans obtained by minimizing the blockage measure are comparable in length to established methods. We agree that an explicit discussion of the scope of the DIG (and of unmodeled constraints such as stability or tool access) would strengthen the paper; we will add a dedicated paragraph on assumptions and limitations in the revised version. revision: yes

Circularity Check

0 steps flagged

No circularity; heuristic defined independently and validated externally

full rationale

The paper introduces the disassembly interference graph (DIG) and part blockage measure as a novel heuristic for expressing obstruction relationships, then applies it to generate subassembly decompositions and plans. These are compared for efficiency against two external state-of-the-art methods on plan length, with no reduction of the central claims to fitted parameters, self-definitions, or self-citation chains. The derivation chain is self-contained against external benchmarks, with no quoted steps that collapse by construction to the inputs.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central claim rests on the assumption that obstruction relationships are faithfully captured by a graph and that the derived blockage measure is a reliable optimization objective. No free parameters are mentioned. The DIG itself is the primary invented modeling construct.

axioms (1)
  • domain assumption Obstruction relationships between assembly parts can be represented as a graph whose structure supports sequence planning.
    Invoked when the paper defines the DIG as the basis for the planner without further derivation in the abstract.
invented entities (2)
  • Disassembly Interference Graph (DIG) no independent evidence
    purpose: To express obstruction relationships between parts.
    Newly introduced as the main contribution.
  • part blockage measure no independent evidence
    purpose: Quantifies blocking to guide minimization during planning.
    Defined as part of the new heuristic.

pith-pipeline@v0.9.0 · 5657 in / 1360 out tokens · 35281 ms · 2026-05-25T00:57:18.346379+00:00 · methodology

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Reference graph

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