Reimagining Robots: The Future of Cybernetic Organisms with Energy-Efficient Designs

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors Strengths:

 

1. Innovative Perspective: The review presents a compelling overview of the future of cybernetic organisms by integrating bio-inspired energy systems and neuromorphic computing. The discussion on liquid-based energy storage and non-Von Neumann computational architectures is particularly forward-thinking and aligns with ongoing research in adaptive robotic systems.

 

2. Thorough Literature Integration: The review successfully synthesizes a broad range of literature, particularly in neuromorphic computing and energy-efficient robotic design. The comparison of neuromorphic chips (Table 1) is a useful addition, providing insight into current technological capabilities and their suitability for different applications.

 

3. Consideration of Challenges: The review acknowledges critical challenges, such as the lack of standardized frameworks for programming spiking neural networks and scalability limitations in neuromorphic chips. This balanced perspective strengthens the credibility of the work.

 

Areas for Improvement:

 

1. Feasibility and Justification of Flow Batteries in Robotics: The claim that NRFBs provide advantages in safety, scalability, and adaptability (line 432) lacks strong empirical support. The bio-inspired approach of using flow batteries to mimic vascular systems for energy distribution needs further justification. Unlike biological systems, where vascular networks transport oxygen necessary for metabolism, robots do not have this constraint. Instead, they can rely on well-established electrical wiring with lower failure rates, easier maintenance, and rapid battery swapping. Clarifying the advantage of flow batteries over traditional wiring or modular battery solutions based on existing research would enhance the discussion.

 

2. Evolutionary Context and Boundary Conditions of Neuromorphic Computing: The review should more carefully consider the evolutionary context of neuromorphic computing. Humans evolved under strict power constraints—the brain operates within a 20W power limit—and neuromorphic architectures emerged through natural selection under these constraints. While the review correctly identifies the value of neuromorphic computing in power-limited environments (such as disaster sites or outer space), it fails to address whether this approach remains optimal in unlimited power scenarios. Most future robotic applications will likely operate near power stations where rapid battery exchange can effectively eliminate power constraints. Under such conditions, conventional computing architectures with higher absolute performance might outperform neuromorphic systems despite being less energy-efficient. The review would benefit from a clearer delineation of the boundary conditions where neuromorphic computing represents the optimal solution versus scenarios where traditional or hybrid approaches might be superior, providing a more nuanced framework for technological selection based on operational context.

 

Final Recommendation:

 

With further clarification on the justification for flow batteries, boundary conditions for neuromorphic superiority, and more empirical evidence for NRFB claims drawn from existing studies, the review would significantly enhance its impact. Strengthening these areas will ensure that it not only presents an ambitious vision but also provides a rigorous foundation for future discussions and research in the field.

Author Response

Summary

Thank you for taking the time to review my manuscript. I sincerely appreciate your constructive feedback, which has been invaluable in refining the clarity, depth, and impact of my work. I have carefully considered each of your suggestions and have revised the manuscript accordingly. Below, I provide a point-by-point response to your comments, highlighting the specific improvements made.

Comment 1: Feasibility and Justification of Flow Batteries in Robotics

The claim that NRFBs provide advantages in safety, scalability, and adaptability (line 432) lacks strong empirical support. The bio-inspired approach of using flow batteries to mimic vascular systems for energy distribution needs further justification. Unlike biological systems, where vascular networks transport oxygen necessary for metabolism, robots do not have this constraint. Instead, they can rely on well-established electrical wiring with lower failure rates, easier maintenance, and rapid battery swapping. Clarifying the advantage of flow batteries over traditional wiring or modular battery solutions based on existing research would enhance the discussion.

Response 1: Thank you for highlighting this concern. I have revised the manuscript to provide a clearer justification for the use of NRFBs in robotics. The updated discussion now explicitly compares flow batteries to conventional wiring and modular battery solutions, emphasizing their advantages in specific use-case scenarios such as dynamic energy allocation, long-duration missions, and decentralized power distribution (Section 3.1). Additionally, I have clarified that while flow batteries offer distinct benefits in energy adaptability, they may not always be the superior choice depending on the operational context. This ensures a balanced discussion that acknowledges both the strengths and the limitations of the approach.

Comment 2: Evolutionary Context and Boundary Conditions of Neuromorphic Computing

The review should more carefully consider the evolutionary context of neuromorphic computing. Humans evolved under strict power constraints—the brain operates within a 20W power limit—and neuromorphic architectures emerged through natural selection under these constraints. While the review correctly identifies the value of neuromorphic computing in power-limited environments (such as disaster sites or outer space), it fails to address whether this approach remains optimal in unlimited power scenarios. Most future robotic applications will likely operate near power stations where rapid battery exchange can effectively eliminate power constraints. Under such conditions, conventional computing architectures with higher absolute performance might outperform neuromorphic systems despite being less energy-efficient. The review would benefit from a clearer delineation of the boundary conditions where neuromorphic computing represents the optimal solution versus scenarios where traditional or hybrid approaches might be superior, providing a more nuanced framework for technological selection based on operational context.

Response 2: I appreciate this valuable insight and have now expanded the discussion to more clearly define the operational contexts where neuromorphic computing is advantageous versus where conventional architectures might be preferable. The revised manuscript (Section 2.1.4) now explicitly outlines boundary conditions where neuromorphic computing excels, particularly in low-power, high-adaptability applications such as autonomous robotics, disaster response, and deep-space exploration. I have also clarified that in high-performance computing scenarios with abundant energy availability, traditional von Neumann architectures remain more practical. This addition strengthens the discussion by ensuring a realistic assessment of neuromorphic computing's applicability rather than an overly generalized claim of superiority.

All changes have been carefully implemented and marked in yellow in the revised manuscript.

Reviewer 2 Report

Comments and Suggestions for Authors

The author presents a completely different approach to build a robot, replacing the von Neumann architecture with memristor-based processors, neuromorphic computing, and spiking neural networks. The author also proposes using liquid batteries for its efficiency. The manuscript is well-written and is very interesting to read. While this could potentially be a groundbreaking proposal, its feasibility is unproven. Therefore, I don't recommend accepting the manuscript in its current form. I suggest the author build a prototype if possible.

Author Response

Response to Reviewer 2 Comments

Summary

Thank you for your review and for recognizing the originality and clarity of my manuscript. I appreciate your constructive feedback regarding the feasibility of the proposed approach. Below, I provide a point-by-point response to your comments, explaining how I have addressed them in the revised version of the manuscript.

Point-by-point response to Comments and Suggestions for Authors

Comment 1: Feasibility of the Proposed Approach

The author presents a completely different approach to build a robot, replacing the von Neumann architecture with memristor-based processors, neuromorphic computing, and spiking neural networks. The author also proposes using liquid batteries for its efficiency. The manuscript is well-written and is very interesting to read. While this could potentially be a groundbreaking proposal, its feasibility is unproven. Therefore, I don't recommend accepting the manuscript in its current form. I suggest the author build a prototype if possible.

Response 1: Thank you for your thoughtful critique. I acknowledge the importance of demonstrating feasibility and have revised the manuscript to strengthen the discussion on real-world implementations of the proposed technologies. Specifically:

• Expanded the discussion on existing robotic systems that utilize neuromorphic computing and bio-inspired energy solutions, showing that these approaches have been partially implemented in prototype systems (Section 2.1.3 and 3.1).
• Clarified the scope of this work, explaining that while a prototype would be an ideal next step, this study focuses on providing a conceptual and theoretical framework that can guide future experimental developments.
• Highlighted ongoing research efforts in developing hybrid neuromorphic computing models and fluid-based energy systems, reinforcing the practicality of this approach.
• Added a section in Future Work (Conclusion section) emphasizing the need for experimental validation and prototype development as the next logical progression of this research.

The revised manuscript now presents a more balanced perspective, acknowledging the challenges while reinforcing the technological potential of the proposed approach.

I appreciate the reviewer’s feedback and have made significant revisions to enhance the discussion of feasibility while clarifying the purpose of this study. While experimental validation is beyond the scope of the manuscript (and requires a very large financial budget for building a full-size prototype), the revised text now better supports the technological viability of the proposed approach. Thank you again for your valuable insights.

Minor refinements were made for clarity and readability, particularly in technical sections.

All changes have been carefully implemented and marked in yellow in the revised manuscript.

Reviewer 3 Report

Comments and Suggestions for Authors

This paper presents a comprehensive and forward-looking vision for the future of robotics, particularly focusing on the integration of bio-inspired energy systems and non-Von Neumann computing architectures. The author successfully frames the discussion within the broader context of cybernetics, drawing on foundational concepts from Norbert Wiener and other pioneers in the field. The paper is well-structured, with clear sections that build logically toward the conclusion.

 

However, there are areas where the manuscript could be strengthened, particularly in terms of empirical validation, concentrated topics, and technical details. The recommendation of this reviewer is “reconsider after major revisions”, the following suggestions are for the authors’ reference.

 

(1) While the paper provides a thorough theoretical discussion, it often lacks concrete experimental results or specific data to support your claims. For example, the discussion of neuromorphic chips (e.g., Loihi, TrueNorth) could benefit from more detailed studies demonstrating their practical implementation in robotics. The section on flow batteries and energy systems also limits discussion of real-world applications or comparative analyses of existing technologies.

 

(2) While the paper is generally well-organized, some sections feel repetitive. For example, the discussion of neuromorphic chips and their challenges is revisited multiple times, which could be streamlined.

 

(3) The paper touches on a wide range of topics, which risks weakening its focus. While this breadth is a strength in terms of vision, it could be more impactful if the author narrows the scope to a few key areas, such as the integration of neuromorphic computing with bio-inspired energy systems, and provides more in-depth analysis of these topics.

 

(4) The conclusion is somewhat empty and should be more specific in outlining the key contributions of the paper and its implications for future research. The section on future work should include more concrete recommendations, such as specific research questions or methodologies.

Author Response

Response to Reviewer 3 Comments

Summary

Thank you for your insightful review and for recognizing the forward-looking vision and structured approach of my manuscript. I appreciate your constructive feedback, which has helped refine the discussion on empirical validation, scope, and technical details. Below, I provide a point-by-point response to your comments, explaining the changes made in the revised version.

Point-by-point response to Comments and Suggestions for Authors

Comment 1: Empirical Validation and Technical Data

While the paper provides a thorough theoretical discussion, it often lacks concrete experimental results or specific data to support your claims. For example, the discussion of neuromorphic chips (e.g., Loihi, TrueNorth) could benefit from more detailed studies demonstrating their practical implementation in robotics. The section on flow batteries and energy systems also limits discussion of real-world applications or comparative analyses of existing technologies.

Response 1: Thank you for pointing this out. While this study is primarily conceptual, I have revised the manuscript to include additional discussions on real-world examples of neuromorphic computing and bio-inspired energy systems in robotics (Sections 2.1.3 and 3.1). These additions:

• Highlight existing implementations of neuromorphic computing in autonomous systems to reinforce feasibility.
• Discuss relevant case studies on flow battery applications in robotic and energy-intensive systems, illustrating their scalability and adaptability.
• Clarify that while direct experimental results are not presented here, this work serves as a theoretical foundation for future empirical research.

These modifications strengthen the manuscript’s credibility while maintaining its forward-looking scope.

Comment 2: Reducing Redundancy

While the paper is generally well-organized, some sections feel repetitive. For example, the discussion of neuromorphic chips and their challenges is revisited multiple times, which could be streamlined.

Response 2: I appreciate this observation and have carefully streamlined repetitive sections. Specifically:

• Merged discussions of neuromorphic chip challenges to eliminate redundancy while maintaining completeness (Sections 2.1.3 and 2.3).
• Ensured that each discussion adds new insights rather than restating previously mentioned ideas.

These changes improve the readability and coherence of the manuscript.

 

Comment 3: Focusing the Scope for Greater Impact

The paper touches on a wide range of topics, which risks weakening its focus. While this breadth is a strength in terms of vision, it could be more impactful if the author narrows the scope to a few key areas, such as the integration of neuromorphic computing with bio-inspired energy systems, and provides more in-depth analysis of these topics.

Response 3: I have refined the scope of the manuscript to enhance its focus:

• Prioritized discussions on neuromorphic computing and bio-inspired energy systems, making them the central themes.
• Reduced tangential discussions on secondary topics, ensuring the manuscript remains well-structured and impactful.
• Provided more in-depth analysis on the integration of these technologies, particularly in Sections 2.1.3 and 3.1.

These refinements ensure a stronger, more cohesive narrative.

Comment 4: Strengthening the Conclusion and Future Work

The conclusion is somewhat empty and should be more specific in outlining the key contributions of the paper and its implications for future research. The section on future work should include more concrete recommendations, such as specific research questions or methodologies.

Response 4: Thank you for this recommendation. I have revised the Conclusion and Future Work section to:

• Clearly summarize the manuscript’s key contributions to neuromorphic computing and bio-inspired energy systems.
• Provide specific future research directions, including:
• Hybrid neuromorphic-von Neumann architectures.
• Self-regulating energy distribution models for robotics.
• Advances in electrolyte chemistry for enhanced flow battery performance.
• Suggest methodologies for future empirical validation, such as prototype testing and comparative performance analysis.

These changes ensure that the conclusion provides clear takeaways and actionable research directions.

Final Recommendation:

I greatly appreciate the reviewer’s feedback and believe the revisions have significantly improved the manuscript’s structure, focus, and empirical grounding. Thank you again for your thorough review and valuable suggestions.

Minor refinements were made for clarity and readability, particularly in technical sections.

All changes have been carefully implemented and marked in yellow in the revised manuscript.

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript has been improved to focus more on the theoretical proposal, and emphasize the need and the challenges for prototyping. My concerns have been addressed, therefore I recommend accepting the manuscript.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have replied to the comments of the previous round of reviews, and have made detailed revisions to the manuscript, recommending acceptance.