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Optimizing towards the best insertion-based error-tolerating joints

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Zhibin Zou

Robotics · Submitted 30 Sep 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We show in simulation that our proposed method can generate insertion-based joints that are tolerant to the given errors. and we present a few simple 3D projections to show that the analysis is still effective beyond 2D cases.

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arXiv ID2209.15147 (version 2)
Pith IDL2HL6RPH, Pith's permanent record of this paper and its review
CitationZhibin Zou. "Optimizing towards the best insertion-based error-tolerating joints." arXiv:2209.15147 (2022). https://pith.science/paper/L2HL6RPH
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,518 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.51 · human participants 0.02 · controlled comparison 0.03 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.05 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We present an optimization-based design process that can generate the best insertion-based joints with respect to different errors, including manipulation error, manufacturing error, and sensing error. We separate the analysis into two stages, the insertion and the after-insertion stability. Each sub-process is discretized into different modes of contacts. The transitions among the contact modes form a directed graph and the connectivity of the graph is achieved and maintained through the manipulation of the socket edge-angle and peg contact-point locations. The analysis starts in 2D with the assumption of point-edge contacts. During the optimization, the edges of the socket are rotated and the points on the peg are moved along the edges to ensure the successful insertion and the stability after insertion. We show in simulation that our proposed method can generate insertion-based joints that are tolerant to the given errors. and we present a few simple 3D projections to show that the analysis is still effective beyond 2D cases.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We present an optimization-based design process that can generate the best insertion-based joints with respect to different errors, including manipulation error, manufacturing error, and sensing error. contribution 1.00 0.94 0.44 asserted 0.99
s1 We separate the analysis into two stages, the insertion and the after-insertion stability. method 0.98 0.89 0.04 asserted 0.98
s2 Each sub-process is discretized into different modes of contacts. method 1.00 0.81 0.02 asserted 1.00
s3 The transitions among the contact modes form a directed graph and the connectivity of the graph is achieved and maintained through the manipulation of the socket edge-angle and peg contact-point locations. method 1.00 0.81 0.08 asserted 1.00
s4 The analysis starts in 2D with the assumption of point-edge contacts. method 0.92 0.77 0.02 asserted 0.98
s5 During the optimization, the edges of the socket are rotated and the points on the peg are moved along the edges to ensure the successful insertion and the stability after insertion. method 1.00 0.90 0.07 asserted 0.98
s6 We show in simulation that our proposed method can generate insertion-based joints that are tolerant to the given errors. and we present a few simple 3D projections to show that the analysis is still effective beyond 2D cases. result 0.99 0.96 0.75 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We present an optimization-based design process that can generate the best insertion-based joints with respect to different errors, including manipulation error, manufacturing error, and sensing error. We separate the analysis into two stages, the insertion and the after-insertion stability. Each sub-process is discretized into different modes of contacts. The transitions among the contact modes form a directed graph and the connectivity of the graph is achieved and maintained through the manipulation of the socket edge-angle and peg contact-point locations. The analysis starts in 2D with the assumption of point-edge contacts. During the optimization, the edges of the socket are rotated and the points on the peg are moved along the edges to ensure the successful insertion and the stability after insertion. We show in simulation that our proposed method can generate insertion-based joints that are tolerant to the given errors. and we present a few simple 3D projections to show that the analysis is still effective beyond 2D cases.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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