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Article
Peer-Review Record

Soft DAgger: Sample-Efficient Imitation Learning for Control of Soft Robots

Sensors 2023, 23(19), 8278; https://doi.org/10.3390/s23198278
by Muhammad Sunny Nazeer 1,2,*, Cecilia Laschi 3 and Egidio Falotico 1,2
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Sensors 2023, 23(19), 8278; https://doi.org/10.3390/s23198278
Submission received: 8 September 2023 / Revised: 28 September 2023 / Accepted: 2 October 2023 / Published: 6 October 2023
(This article belongs to the Special Issue Kinematically Redundant Robots: Sensing and Control)

Round 1

Reviewer 1 Report

please find it in attachment. 

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

The article is a very relevant subject and written at a high level.

However, there are some points that need to be corrected.

 

The title of the article completely corresponds to it.

However, the article has a few points that need to be corrected:

- The authors should disclose in more detail the motivation for conducting this research. It is possible to demonstrate directions where such training may be needed in practice.

- Equation 3 should be divided into two equations.

- The conclusion should provide more accurate and compelling results with which the proposed model can reproduce a certain trajectory.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

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