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Neuro-robots mimic human brains but face failures due to imperfect data and AI explainability limits. This review explores neuro-robotic technology, failures, and proposes future research for enhanced robot explainability.

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explainabilityexplainable AI (X-AI)explainable neuro-robotsneuro-robotic failuresneuro-robotic modelsneuro-robotic systemsresponsible neuro-robots

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Neuroscience

Background:

  • Neuro-robots are autonomous machines inspired by human brain architecture and cognition.
  • Current regulations, like the EU's Convention on Roboethics 2025, mandate traceability for robots.
  • Increasing complexity and real-world operation lead to neuro-robotic failures, highlighting AI explainability challenges.

Purpose of the Study:

  • To review the technological advancements in neuro-robots.
  • To analyze the causes and implications of neuro-robotic failures.
  • To investigate the relationship between AI explainability and neuro-robot performance.

Main Methods:

  • Literature review of neuro-robotic technology and failure case studies.
  • Analysis of existing explainable AI (XAI) research.
  • Comparative study of AI explainability limitations in neuro-robots.

Main Results:

  • Neuro-robotic technology has advanced significantly, mirroring brain functions.
  • Failures in neuro-robots are inevitable due to imperfect data and AI limitations.
  • Existing explainable AI approaches may inherently limit neuro-robot explainability.

Conclusions:

  • Neuro-robotic failures necessitate a deeper understanding of their underlying AI.
  • Current explainable AI research presents challenges for achieving full neuro-robot transparency.
  • Future research should focus on novel approaches to enhance neuro-robot explainability and reliability.