PONE-D-24-43885A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithmPLOS ONE

Dear Dr. Wu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. According to the feedback from the reviewers, there are still several problems that need to be improved:1) The abstract and conclusion need further refinement. Meanwhile, a discussion on the application and limitation is suggested.2) The novelty of this manuscript should be clearly clarified.3) Most of the figures in this manuscript is not clear and a further adjustment is expected.4) More details about the methodology and experimental setup are missing.

Please submit your revised manuscript by Dec 19 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Longhui Qin, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The abstract need to be rewritten in more professional way, mention the gap of the science and the importance and the logic of this study, the methodology more detailed and the most important results such as quantities or percentage of improvement.

The conclusion needs to cover the most significant achievements, including some comparison in percentage or another professional way.

Considering to compare tables 1 and 2 data would provide some more useful information.

Consider reproduce the unclear pictures. Please revise all figures and make sure texts also have a good resolution.

Reviewer #2: The manuscript presents a novel approach for robotic arm trajectory learning using a Gaussian Mixture Model (GMM) with a k-value selection algorithm. This innovative combination addresses key challenges in trajectory accuracy. The research is technically sound and well-supported by experiments, but several improvements are suggested:

1. Clarify the Novelty: Expand on the unique aspects of the k-value selection method compared to existing approaches.

2. Methodology Details: Add further explanation for the mathematical steps and consider comparisons with other k-selection methods.

3. Experimental Clarity: Provide more details on the physical experiment setup and improve figure labeling.

4. Applications and Limitations: Discuss broader applications and potential limitations of the method.

5. Statistical Validation: Include confidence intervals to enhance the statistical robustness of the findings.

With these minor adjustments, the manuscript would be ready for publication in PLOS ONE, offering a valuable contribution to robotic trajectory learning.

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Reviewer #1: No

Reviewer #2: No

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Attachments

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Submitted filename: OSQ PEER REVIEWER 2.pdf

PONE-D-24-43885R1A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithmPLOS ONE

Dear Dr. Wu,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

 Most of the reviewers' comments have been addressed. However, quality of all the figures seems not enough. It is suggested to improve them further, e.g., higher resolution. The font size in figures should be similar to that in the context. 

Please submit your revised manuscript by Feb 19 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Longhui Qin, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step.

A novel trajectory learning method for robotic arms based on Gaussian Mixture Model and k-value selection algorithm

PONE-D-24-43885R2

Dear Dr. Wu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Longhui Qin, Ph.D.

Academic Editor

PLOS ONE

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Reviewers' comments: