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Benchmarking Simulated Robotic Manipulation through a Real World Dataset

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Jack Collins, Jessie McVicar, David Wedlock, Ross Brown, David Howard, Jürgen Leitner

Robotics · Submitted 5 Nov 2019

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

The authors' main claim We present a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset.

how it is stated · asserted 0.98 read the record →

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arXiv ID1911.01557 (version 2)
Pith IDUCNWACXR, Pith's permanent record of this paper and its review
CitationDavid Howard, David Wedlock, Jack Collins, Jessie McVicar, J\"urgen Leitner, Ross Brown. "Benchmarking Simulated Robotic Manipulation through a Real World Dataset." arXiv:1911.01557 (2019). https://pith.science/paper/UCNWACXR
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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, 4,238 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.13 · human participants 0.08 · controlled comparison 0.21 · randomized assignment 0.02 · states quantitative result 0.06 · compares with prior results 0.44 · mentions code or data availability 0.95.

The abstract, sentence by sentence

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

We present a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. Users are given various simulated manipulation tasks with assigned protocols having the objective of replicating the real world results of a recorded dataset. The benchmark comprises of a range of metrics used to characterise the successes of submitted environments whilst providing insight into their deficiencies. We apply our benchmark to two simulation environments, PyBullet and V-Rep, and publish the results. All materials required to benchmark an environment, including protocols and the dataset, can be found at the benchmarks' website https://research.csiro.au/robotics/manipulation-benchmark/.

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 a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. contribution 1.00 0.90 0.20 asserted 0.98
s1 Users are given various simulated manipulation tasks with assigned protocols having the objective of replicating the real world results of a recorded dataset. method 0.87 0.22 0.06 asserted 0.99
s2 The benchmark comprises of a range of metrics used to characterise the successes of submitted environments whilst providing insight into their deficiencies. method 0.58 0.64 0.08 asserted 1.00
s3 We apply our benchmark to two simulation environments, PyBullet and V-Rep, and publish the results. contribution 0.45 0.95 0.12 asserted 1.00
s4 All materials required to benchmark an environment, including protocols and the dataset, can be found at the benchmarks' website https://research.csiro.au/robotics/manipulation-benchmark/. contribution 0.85 0.48 0.04 asserted 1.00

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from simulation or computation 0.50.

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 a benchmark to facilitate simulated manipulation; an attempt to overcome the obstacles of physical benchmarks through the distribution of a real world, ground truth dataset. Users are given various simulated manipulation tasks with assigned protocols having the objective of replicating the real world results of a recorded dataset. The benchmark comprises of a range of metrics used to characterise the successes of submitted environments whilst providing insight into their deficiencies. We apply our benchmark to two simulation environments, PyBullet and V-Rep, and publish the results. All materials required to benchmark an environment, including protocols and the dataset, can be found at the benchmarks' website https://research.csiro.au/robotics/manipulation-benchmark/.

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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

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