Physics-constrained learning framework for trustworthy microrobot navigation autonomy

Area of Science:

• Microrobotics
• Artificial Intelligence
• Machine Learning

Background:

• Machine learning enables autonomous microrobot navigation but relies on "black-box" neural networks (NNs), raising trustworthiness concerns, especially in biomedical applications.
• Current methods lack explainability and rigorous safety guarantees for complex navigation tasks.

Purpose of the Study:

• To develop a physics-constrained learning framework for trustworthy microrobot navigation.
• To address the critical concerns regarding the reliability and explainability of current learning-based navigation policies.

Main Methods:

• Proposed a novel framework embedding deterministic physical laws and safety rules into NN architectures.
• Explicitly encoded kinematic relationships and monotonic constraints as structural inductive biases.
• Developed a physics-constrained learning approach to guarantee navigation policies operate within trustworthy domains.

Main Results:

• Demonstrated autonomous target acquisition and collision-free navigation for microrobots in long-term, long-distance missions.
• Validated the framework's reliability and explainable nature through experimental results.
• Showcased the ability of microrobots to navigate complex and disturbed environments autonomously.

Conclusions:

• The physics-constrained learning framework bridges the gap between data-driven performance and safety requirements in microrobotics.
• This approach enhances the trustworthiness of artificial intelligence-empowered microrobots for critical applications.
• The explainable nature of the framework ensures reliable and safe autonomous navigation.