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Peer-Review Record

Deep Reinforcement Learning for Model Predictive Controller Based on Disturbed Single Rigid Body Model of Biped Robots

Machines 2022, 10(11), 975; https://doi.org/10.3390/machines10110975
by Landong Hou 1, Bin Li 2,*, Weilong Liu 2, Yiming Xu 1, Shuhui Yang 2 and Xuewen Rong 3
Reviewer 1:
Reviewer 2: Anonymous
Reviewer 3:
Machines 2022, 10(11), 975; https://doi.org/10.3390/machines10110975
Submission received: 19 September 2022 / Revised: 21 October 2022 / Accepted: 22 October 2022 / Published: 26 October 2022
(This article belongs to the Topic Intelligent Systems and Robotics)

Round 1

Reviewer 1 Report

Please see PDF attached. Thank you. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Hou et al. present a DSRB-PMC method that take the mass of legs into consideration for bipedal robots. The method and results are clearly presented. The reviewer has two questions as listed below.

1. If the portion of leg mass exceeds 30%, what would be the possible solution to predict the disturbances?

2. How to potentially eliminate the static velocity error of this DSRB-PMC method?

3. Is the current method suitable for quadruped robots?

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

The paper has a good scientific soundness and clearly explains the research.

Adding a little more detail on touchdown policies 1, 2 and 3 may help to understand in detail what they consist of and why it is necessary to switch from one to the other.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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