Dear Dr. Zhang,

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.

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ACADEMIC EDITOR: The manuscript presents many major concerns as highlighted by both reviewers. However, I see merits on the work. Please, address all the concerns rised by the Experts and provide an updated version of the manuscript.

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Andrea Tigrini, Ph.D.

Academic Editor

PLOS ONE

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Additional Editor Comments (if provided):

The manuscript presents many major concerns as highlighted by both reviewers. However, I see merits on the work. Please, address all the concerns rised by the Experts and provide an updated version of the manuscript.

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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?

Reviewer #1: Partly

Reviewer #2: No

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

Reviewer #1: No

Reviewer #2: No

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: No

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

Reviewer #1: Yes

Reviewer #2: No

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Reviewer #1: The manuscript proposes a DDPG-based autonomous learning algorithm for drug retrieval robots, it addresses challenges in grasping accuracy and real-time performance.

While the methodology is interesting, the manuscript requires significant improvements in clarity, technical rigor, and validation to meet publication standards. Below are detailed concern and recommendations.

1. The "detection constraints" (central to the title) are never formally defined. It is not clear how boundaries are analysed (Section 3.1). The constraints must be mathematically defined and how much candidate region extraction boost performance should be evaluated and justified.

2. Equation (8) assumes a pinhole camera model but omits lens distortion correction, critical for real-world accuracy. Please take this into account.

3. The adaptive noise exploration (Eq. 17) lacks justification for threshold δ and decay factor α. Please provide these justifications.

4. "The proposed method has more accurate grasping poses" in the Abstract is not statistically tested. It should be added.

5. Latency (0.9s) is claimed but not benchmarked against robot control cycles. Please compare latency to robot actuator response times (e.g., typical 10–100ms).

6. The autonomous learning component is compared only to vanilla DDPG, not state-of-the-art alternatives (e.g., SAC - Soft Actor-Critic, TD3 - Twin Delayed Deep Deterministic Policy Gradient). Please add alternative comparisons.

7. In the introduction challenging grasping problems such as those showed in the paper “Automating the hand layup process: On the removal of protective films with collaborative robots” https://doi.org/10.1016/j.rcim.2024.102899, should be mentioned.

8. The manuscript reveal a limited real-world testing, experiments use public datasets (Cornell) and simple simulations (rectangular prism). Real robot trials are absent. Test on a physical robot with varied drug geometries (vials, blister packs) is recommended.

9. The DDPG training in Section 4.3.2 uses a simplistic rectangular prism, ignoring real drug variability. Please validate autonomous grasping in a physics simulator with randomized drug poses.

10. Superior accuracy (92.1% vs. 86.4% in Table 2) is presented without technical explanation. Is this due to boundary analysis or DDPG? Please clarify. Please attribute gains to candidate region extraction (reduced search space) and adaptive DDPG exploration.

11. Statistical Reporting in Fig. 12 seems to be inadequate; the success rates (Fig. 12) lack variance measures. Please add confidence intervals to Fig. 12; report mean ± std. dev. in tables.

12. Results are disconnected from real-world pharmacy demands (e.g., FDA standards for medication retrieval accuracy) Please discuss applicability in hospital settings, citing safety margins (e.g., <x% critical=" " drugs=" " error=" " for=" "></x%>

Reviewer #2: The proposed work aimed at providing a method for optimizing autonomous robot grasping for drug retrieval using reinforcement learning, specifically deep deterministic policy gradient (DDPG) algorithm. I have some major concerns that mainly regard the writing and the methodological rigor. My major doubt regards the novelty of the proposed approach: it is not clear if a novel approach was proposed or if an already known approach was applied to the specific problem of drug retrieval. This should have been better clarified throughout the text. Moreover, the description of the performed experiments is lacking, thus hampering a thorough evaluation of the methodological power. Point by point comments are provided in the following.

- Abstract should report some numerical results.

- Aim of the study should be clearly stated and should be more specific about the novelty.

- The Related Work section is not informative and needs to be rewritten. Related Works should report in brief the relevant literature in the field of the deep reinforcement learning strategies and the state of the art and latest relevant methods for the field of drug retrieval with robot grasping. Replicating theoretical formulas already consolidated makes the manuscript very cumbersome.

- In the references section there are no references regarding automatic drug retrieval with robot grasping. This raises concerns about whether the research question is grounded in current developments or unmet needs in the field. Recent work in pharmacy automation and robotic manipulation should be cited to better contextualize the relevance and novelty of the contribution.

- The Methods (section 3), as it is written, do not allow to understand what part of the proposed method is new with respect to the existing literature.

- In section 3.2 it is stated “ nx ny is mapped to the pixel positions in the image according to the formula (Fig. 3).”, but no equation is cited and a figure is mentioned instead. Which “formula” is this sentence referring to?

- In the results section 4.3.1, how was the detection accuracy computed? What does “The images of the candidate grasping region are used as the test images for the proposed network model” mean? How was the dataset of the “(2) Actual drug-grasping point detection results” experiment obtained? Materials used and performed experiments must be carefully explained.

- The basis for the statement “The proposed method can find a suitable position for grasping the medicine from the image, and the predicted grasping rectangular box can sufficiently reflect the posture suitable for grasping the medicine.” is unclear. Is this claim supported by quantitative evaluation? The red lines in Figure 9 are difficult to be interpreted.

- It is not specified why the algorithms used for comparisons where selected: where those algorithms the gold standards? Moreover, are those algorithms/approaches open source? It should be specified if such algorithms where implemented to carry on comparison experiments.

- Formatting of references should be carefully assessed. Reference n 29 is wrongly formatted (i.e., first names are reported instead of last names).

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what does this mean? ). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

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An algorithm for drug retrieval based on robot-grasping detection constraints and DDPG autonomous learning

PONE-D-25-23921R1

Dear Dr. Zhang,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Andrea Tigrini, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Authros addressed all the concerns and the manuscript can be accepted for publication.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

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

Reviewer #3: Yes

Reviewer #4: Yes

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3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #3: N/A

Reviewer #4: No

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

The PLOS Data policy

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #3: Thank you for your detailed and thoughtful responses to reviewer comments. I appreciate the effort you have made to address the concerns raised. You have effectively incorporated the major suggestions.

Overall, I am satisfied with the current version of your manuscript. The revisions have strengthened the work, and in my view, it is now well-prepared for publication.

Wishing you success with your ongoing and future research endeavors.

Best regards

Reviewer #4: Dear Authors,

I have found that you've addressed the feedback thoroughly and explicitly including latency clarification, comparitive baselines for algorithms, real world experiments and clarifications of performance gain. The manuscript is technically sound and clearly structured. The problem statement is thoroughly addressed with the proposed methodology. A statistical analysis of the results provided is acceptable, though it could benefit from a deeper quantitative analysis. Overall, I find the manuscript meeting the necessary standards for PLOS One.

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what does this mean? ). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #3: No

Reviewer #4: Yes:  Akshay Aggarwal

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