Evidence map›Paper›PMID 36105761›Full record

ArticleFrontiers in robotics and AI2022

Making Bipedal Robot Experiments Reproducible and Comparable: The Eurobench Software Approach.

Anthony Remazeilles, Alfonso Dominguez, Pierre Barralon, Adriana Torres-Pardo, David Pinto, Felix Aller, Katja Mombaur, Roberto Conti, Lorenzo Saccares, Freygardur Thorsteinsson and 6 more

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Anthony RemazeillesTECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastián, Spain.
Alfonso DominguezTECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastián, Spain.
Pierre BarralonTECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastián, Spain.
Adriana Torres-PardoSpanish National Research Council (CSIC), Cajal Institute, Neural rehabilitation Group, Madrid, Spain.
David PintoSpanish National Research Council (CSIC), Cajal Institute, Neural rehabilitation Group, Madrid, Spain.
Felix AllerOptimization, Robotics and Biomechanics, Institute of Computer Engineering, Heidelberg University, Heidelberg, Germany.
Katja MombaurDepartment of Systems Design Engineering, Department of Mechanical and Mechatronics Engineering, University of Waterloo, Waterloo, ON, Canada.
Roberto ContiIUVO S. r.l., Pontedera, Italy.
Lorenzo SaccaresIUVO S. r.l., Pontedera, Italy.
Freygardur ThorsteinssonÖssur Iceland ehf, Reykjavik, Iceland.
Erik PrinsenDepartment of Biomechanical Engineering, Techmed Centre, Roessingh Research and Development, University of Twente, Enschede, Netherlands.
Alberto CantónCenter for Clinical Neuroscience, Hospital Los Madroños, Madrid, Spain.
Javier CastillaCenter for Clinical Neuroscience, Hospital Los Madroños, Madrid, Spain.
Clara B Sanz-MorèreCenter for Clinical Neuroscience, Hospital Los Madroños, Madrid, Spain.
Jesús TorneroCenter for Clinical Neuroscience, Hospital Los Madroños, Madrid, Spain.
Diego TorricelliSpanish National Research Council (CSIC), Cajal Institute, Neural rehabilitation Group, Madrid, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study describes the software methodology designed for systematic benchmarking of bipedal systems through the computation of performance indicators from data collected during an experimentation stage. Under the umbrella of the European project Eurobench, we collected approximately 30 protocols with related testbeds and scoring algorithms, aiming at characterizing the performances of humanoids, exoskeletons, and/or prosthesis under different conditions. The main challenge addressed in this study concerns the standardization of the scoring process to permit a systematic benchmark of the experiments. The complexity of this process is mainly due to the lack of consistency in how to store and organize experimental data, how to define the input and output of benchmarking algorithms, and how to implement these algorithms. We propose a simple but efficient methodology for preparing scoring algorithms, to ensure reproducibility and replicability of results. This methodology mainly constrains the interface of the software and enables the engineer to develop his/her metric in his/her favorite language. Continuous integration and deployment tools are then used to verify the replicability of the software and to generate an executable instance independent of the language through dockerization. This article presents this methodology and points at all the metrics and documentation repositories designed with this policy in Eurobench. Applying this approach to other protocols and metrics would ease the reproduction, replication, and comparison of experiments.

Indexed as

algorithmbenchmarkingexoskeletonhumanoidperformance indicatorreplicabilitysoftware

Identifiers

PMID36105761
PMCPMC9465443

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.