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Grasp success prediction with quality metrics

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Carlos Rubert, Daniel Kappler, Jeannette Bohg, Antonio Morales

Robotics · Submitted 10 Sep 2018

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

The authors' main claim Our results shows the proposed prediction model has success rate of 76\%.

how it is stated · asserted 1.00 read the record →

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arXiv ID1809.03276 (version 1)
Pith IDKRJLFUE2, Pith's permanent record of this paper and its review
CitationAntonio Morales, Carlos Rubert, Daniel Kappler, Jeannette Bohg. "Grasp success prediction with quality metrics." arXiv:1809.03276 (2018). https://pith.science/paper/KRJLFUE2
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The compiled record

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, 5,935 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.33 · human participants 0.03 · controlled comparison 0.08 · randomized assignment 0.02 · states quantitative result 0.99 · compares with prior results 0.09 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Current robotic manipulation requires reliable methods to predict whether a certain grasp on an object will be successful or not prior to its execution. Different methods and metrics have been developed for this purpose but there is still work to do to provide a robust solution. In this article we combine different metrics to evaluate real grasp executions. We use different machine learning algorithms to train a classifier able to predict the success of candidate grasps. Our experiments are performed with two different robotic grippers and different objects. Grasp candidates are evaluated in both simulation and real world. We consider 3 different categories to label grasp executions: robust, fragile and futile. Our results shows the proposed prediction model has success rate of 76\%.

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 Current robotic manipulation requires reliable methods to predict whether a certain grasp on an object will be successful or not prior to its execution. background 1.00 0.04 0.02 asserted 1.00
s1 Different methods and metrics have been developed for this purpose but there is still work to do to provide a robust solution. gap 0.84 0.04 0.02 asserted 0.99
s2 In this article we combine different metrics to evaluate real grasp executions. contribution 0.84 0.92 0.03 asserted 0.95
s3 We use different machine learning algorithms to train a classifier able to predict the success of candidate grasps. method 0.96 0.94 0.03 asserted 0.97
s4 Our experiments are performed with two different robotic grippers and different objects. method 0.99 0.91 0.02 asserted 0.98
s5 Grasp candidates are evaluated in both simulation and real world. method 0.99 0.91 0.02 asserted 0.98
s6 We consider 3 different categories to label grasp executions: robust, fragile and futile. method 0.95 0.85 0.03 asserted 0.98
s7 Our results shows the proposed prediction model has success rate of 76\%. result 1.00 0.97 0.83 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.80 · evidence from observation or experiment 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
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

Current robotic manipulation requires reliable methods to predict whether a certain grasp on an object will be successful or not prior to its execution. Different methods and metrics have been developed for this purpose but there is still work to do to provide a robust solution. In this article we combine different metrics to evaluate real grasp executions. We use different machine learning algorithms to train a classifier able to predict the success of candidate grasps. Our experiments are performed with two different robotic grippers and different objects. Grasp candidates are evaluated in both simulation and real world. We consider 3 different categories to label grasp executions: robust, fragile and futile. Our results shows the proposed prediction model has success rate of 76\%.

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What this paper does with the arXiv papers it cites

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

What it cites

Uses the method or tool of 1 paper

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